![Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil,
Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018;
Published: 19 October 2018
����������
�������
Abstract: Efficient decision making based on business
intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development
of information and communication
technology has made collection and analysis of big data
essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA).
However, many of these studies are
not linked to BI, as companies do not understand and utilize the
concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review
the literature on BI, big data,
and BDA to show that they are not separate methods but an
integrated decision support system.
Second, we explore how businesses use big data and BDA
practically in conjunction with BI through
a case study of sorting and logistics processing of a typical
courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-16-320.jpg)
![velocity, and variety, or the “3Vs” [1–4], which have recently
been expanded to “5Vs” with the addition
of veracity/verification and value [5–10].
There are numerous multi-dimensional methods for choosing
how much data to gather and how
to analyze and utilize the data. In brief, the methodology for
extracting valuable information and
taking full advantage of it could be more important than the
data’s quality and quantity. A substantial
amount of research has been devoted to establishing and
developing theories concerning big data,
Sustainability 2018, 10, 3778; doi:10.3390/su10103778
www.mdpi.com/journal/sustainability
http://www.mdpi.com/journal/sustainability
http://www.mdpi.com
https://orcid.org/0000-0003-3698-4665
http://www.mdpi.com/2071-
1050/10/10/3778?type=check_update&version=1
http://dx.doi.org/10.3390/su10103778
http://www.mdpi.com/journal/sustainability
Sustainability 2018, 10, 3778 2 of 15
BDA, and BI to address this need, but it is still challenging for
a company to find, understand, integrate,
and use the findings of these studies, which are often conducted
independently and cover only select
aspects of the subject.
BDA refers to the overall process of applying advanced analytic
skills, such as data mining,
statistical analysis, and predictive analysis, to identify patterns,](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-18-320.jpg)
![correlations, trends, and other useful
techniques [11–15]. BDA contributes to increasing the
operational efficiency and business profits,
and is becoming essential to businesses as big data spreads and
grows rapidly.
BI is a decision support system that includes the overall process
of gathering extensive data,
extracting useful data, and providing analytical applications. In
general, BI has three common
technological elements: a data warehouse integrating an online
transaction processing system;
a database addressing specific topics; online analytical
processing that is used to analyze data in
multi-dimensions in order to use those data; and data mining,
which involves a series of technological
methods for extracting useful knowledge from the gathered data
[16–20].
Some areas of BI and BDA, such as data analysis and data
mining, overlap. This is to be expected,
as the raw data in BI have recently expanded to become big data
in volume and scope. This has
necessitated reorganization of the field and concepts of BI to
provide business insights and enable
better decision making based on BDA [21]. Although BI and
BDA are generally studied independently,
it is challenging and often unnecessary to distinguish between
the two concepts when performing
business tasks.
Given the cost of gathering and analyzing big data, it is
important to identify what data to collect,
the range of the data, and the most cost-effective purpose of the
data using BI. For this purpose, it is
effective to understand and apply the methodology based on](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-19-320.jpg)
![experiences of companies shared through
a case study. Therefore, the present study has the following
aims. First, we explore the meaning of BI,
big data, and BDA through a literature review and show that
they are not separate methods, but rather
an organically connected and integrated decision support
system. Second, we use a case study to
examine how big data and BDA are applied in practice through
BI for greater understanding of the
topic. The case study is conducted on a large and rapidly
growing courier service in the logistics
industry, which has a long history of research. In particular, we
examine how the company efficiently
allocates vehicles in hub terminals by collecting, analyzing, and
applying big data to make informed
decisions quickly, as well as uses BI to enhance productivity
and cost-effectiveness.
The rest of the paper proceeds as follows. Section 2 reviews the
research background and literature
related to BI, big data, and BDA. Section 3 presents the case
study for the company and industry and
discusses the case in detail. Finally, Section 4 concludes by
discussing the implications and directions
for future research.
2. Literature Review
Big data have become a subject of growing importance,
especially since Manyika et al. pointed out
that they should be regarded as a key factor to increase
corporate productivity and competitiveness [22].
Many researchers have shown interest in big data, as the rapid
development of information and
communication technology (ICT) generates a significant amount
of data. This has led to lively](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-20-320.jpg)
![discussions about the collection, storage, and application of
such data. In 2012, Kang et al. argued that
the value of big data lies in making forecasts by recognizing
situations, creating new value, simulating
different scenarios, and analyzing patterns through analysis of
the data on a massive scale [23]. In 2011,
only 38 studies related to big data and BDA were listed in the
Science Citation Index Expanded (SCIE),
Social Science Citation Index (SSCI), Arts & Humanities
Citation Index (AHCI), and Emerging Sources
Citation Index (ESCI), but in 2012, this number increased to 92,
and then rapidly increased to 1009 in
2015 and 3890 in 2017 [24].
Sustainability 2018, 10, 3778 3 of 15
2.1. Toward an Integrated Understanding of Big Data, BDA, and
BI
The research boom regarding big data has led to the
development of BDA, through which
valuable information is extracted from a company’s data.
Companies are well aware of the increasing
importance and investment need for BDA, as shown by Tankard
[25], who claimed that a company can
secure higher market share than its rivals and has the potential
to increase its operating profit margin
ratio by up to 60% by using big data effectively [25,26]. In the
logistics industry, big data are used
more widely than ever for supporting and optimizing
operational processes, including supply chain
management. Big data have been instrumental in developing
new products and services, planning
supply, managing inventory and risks, and providing customized](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-21-320.jpg)
![services [26–29].
BI has a longer history of research than that of big data. In
1865, Richard Millar Devens mentioned
the concept in the Cyclopaedia of Commercial and Business
Anecdotes [30], after which Luhn began
using it in its modern meaning in 1958 [31]. Thereafter, Vitt et
al. defined BI as an information system
and method for decision making that incorporates the four-step
cycle of analysis, insight, action,
and performance measurement [32]. Solomon suggested a
framework of BI and argued that research
in the area was necessary [20]. Then, Turban et al. [33]
expanded the scope of research to embrace
data mining, warehousing and acquisition, and business
analysis, and a growing number of studies
followed. Miškuf and Zolotová studied BI using Cognos—a BI
solution system adopted by IBM—and
the case of U.S. Steel to ascertain how to best apply
enterprise/manufacturing intelligence to manage
manufacturing data efficiently [30]. Van-Hau pointed out the
lack of a general framework in BI that
would allow businesses to integrate results and systematically
use them, as well as discussed issues
that needed to be researched further [34]. In summary, the
concept of BI has been expanding with
regard to application systems and technologies that support
enterprises in making better choices by
gathering, storing, analyzing, and accessing data more
effectively [35].
Previous research has dealt mostly with management and
decision support systems and
applications in BI, as well as technological aspects such as
algorithms and computing for big data
and BDA. However, the research areas are broadening, and](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-22-320.jpg)
![topics are becoming more diverse
based on different macroeconomic environments, pace of
technological progress, and division of
the research field. Therefore, many studies on BI, big data, and
BDA have been conducted separately.
More importantly, big data research has a relatively short
history, as it only started attracting significant
attention since around 2012, when rapid development of ICTs
led to discussions on how to gather
and use the unprecedented amount of data generated. On the
other hand, BI has long been a point of
interest among researchers.
The boundaries between these concepts—big data, BDA, and
BI—are often unclear and ambiguous
for companies. Generally, BI consists of an information value
chain for gathering raw data,
turning these data into useful information, management decision
making, driving business results,
and raising corporate value [36]. However, considering that
“raw data” have been expanded to “big
data” owing to the development of ICT and data storage, it is
safe to say that BI and big data/BDA are
presently not independent methods but organically coexist as an
integrated decision support system,
incorporating all processes from data gathering to management
decision making in business.
As research interest in big data began to grow since 2012, Chen
et al. grouped previous works
in the literature into BI and analytics and divided the evolution
process of the subject into stages to
examine the main characteristics and features of each stage
[37]. Subsequently, Wixom et al. proposed
the necessity of studying BI—including big data/BDA—and
business analytics to address changes](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-23-320.jpg)
![in the field, since there was increasing awareness about the use
and need of big data after the BI
Conference of the Communications of the Association for
Information Systems in 2009 and 2010 [38].
Fan et al. studied BI in the marketing sector in a big data
environment and concluded that big data
and BDA are disruptive technologies that reorganize the
processes of BI to gain business insights for
better decision making [21]. In addition, Bala and Balachandran
defined cloud computing and big
data as the two of the most important technologies in recent
years and explored the improvement
of decision-making processes through BI by integrating these
two key technologies for storing and
Sustainability 2018, 10, 3778 4 of 15
distributing data using cloud computing [39]. These cases
illustrate that an increasing number of
researchers are approaching BI and big data/BDA as an
integrated concept.
2.2. In-Depth Research through Case Studies
The growing interest in big data/BDA and rapid development in
this area have strengthened
BI as a decision support system, thereby promoting corporate
management and enhancing business
value by providing more valuable information to generate
innovative ideas for new products and
services. This has led to a rise in customer satisfaction,
improved inventory and risk management,
improved supply chain risk management, creation of
competitive information, and provision of](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-24-320.jpg)
![real-time business insights [26–29,40–42].
Considering the short lifecycle of big data and their use in
companies, there are numerous,
multi-dimensional methods for deciding how much data to
gather and how to analyze and utilize the
data speedily and effectively. As David et al. emphasized in The
Parable of Google Flu: Traps in Big Data
Analysis, the essential element is turning data into valuable
information, not the quantity of data or
new data itself [43]. It is thus important to establish a database
of integrated convergent knowledge
and continue to develop this by accumulating knowledge and
experiences through case studies based
on practical use that apply the principals of BI and big
data/BDA effectively. Below, we list examples
of successful studies on the use and application of big
data/BDA in practice.
• Zhong et al. examined a big data approach that facilitates
several innovations that can guide
end-users to implement associated decisions through radio
frequency identification (RFID) to
support logistics management with RFID-Cuboids, map tables,
and a spatiotemporal sequential
logistics trajectory [44].
• Marcos et al. studied both the environment and approaches to
conduct BDA, such as data
management, model development, visualization, user
interaction, and business models [45].
• Kim reported several successful cases of big data application.
Examples include analysis of
competing scenarios through 66,000 simulated elections
conducted per day to understand the](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-25-320.jpg)
![decisions of individual voters during the 2012 reelection
campaign of former US president Barack
Obama and delivery routes and time management based on
vehicle and parcel locations adopted
by UPS, a US courier service company [46].
• Wang et al. redefined big data business analytics of logistics
and supply chain management as
supply chain analytics and discussed its importance [47].
• Queiroz and Telles studied the level of awareness of BDA in
Brazilian companies through surveys
conducted via questionnaires and proposed a framework to
analyze companies’ maturity in
implementing BDA projects in logistics and supply chain
management [48].
• Hopkins analyzed the impact of BDA and Internet of things
(IoT), such as truck telematics and
geo-information in supporting large logistics companies to
improve drivers’ safety and operating
cost-efficiency [49].
The above examples of big data/BDA used by governments or
corporations, as well as entities
dealing with methods in either specific or general areas, make it
clear that there is an abundance of
studies on the need for and efficiency of big data. However, big
data and BDA have not been studied
until recently, and few studies use real corporate examples—
especially in the logistics industry—that
provide valuable business insights through detailed methods and
results.
Researchers should endeavor to provide second-hand experience
through specific case studies](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-26-320.jpg)
![to KRW 79,954,478 million in 2017, an increase of 21.85%
from KRW 65,617,046 million in 2016,
and a massive 107.69% increase from 2013 [50]. The courier
service industry has become the biggest
beneficiary of this dramatic increase in the volume of goods
transported and is a suitable yardstick to
measure the growth of the logistics industry [51,52].
Traditionally, logistics was considered a support
industry for manufacturing and consumption and was mainly
perceived as a cost, but it has since
emerged as the main industry connecting producers and
consumers. Manufacturing corporations
regard supply expansion based on ICT to meet consumers’
demands as a key growth strategy, and the
courier service industry has shown remarkable growth owing to
the sharp increase in the need for
parcel transportation [53].
A courier service is generally defined as comprising the entire
process of transportation,
from receiving a parcel to packaging, transporting, and
delivering the parcel to the final destination
under the transporter’s responsibility and at the customer’s
request [54,55]. The courier service
industry usually faces oligopolistic market competition, as it is
an enormous service system
that requires huge initial investment. Courier service companies
are normally large operational
organizations that deal with large amounts of cargo, hub
terminals, general information systems, and a
wide range of transportation vehicles and consist of a
complicated network of labor and equipment [51].
Davis previously examined the usefulness of courier services by
using information technology
in the logistics industry [56]. DeLone and McLean showed that](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-28-320.jpg)
![a successful information system
environment is a significant factor influencing user satisfaction
as it models its influences on
individuals and organizations [57]. Kim et al. focused on the
use of transportation routes,
freight distribution centers, and brokerage points for efficient
parcel transportation via main roads [58].
Visser and Lanzendorf [59] analyzed the effects of business-to-
consumer (B2C) e-commerce for cargo
transportation, revealing that an increase in the demand for
courier services leads to changes in
freight per ton, distance, size, and fill rate of trucks. The
authors illustrated the relationship between
consolidation and transportation routes in courier companies
[59]. Jeong et al. discussed the allocation
of service centers to terminals with a given number of cargo
terminals and locations [60], while Goh
and Min examined the time of delivery by the capacity of cargo
terminals [61]. Meanwhile, Sherif et al.
presented an integrated model of the number and location of
warehouses, allocation of customers to
warehouses, and number and routes of vehicles to minimize
transportation cost, fixed cost, operational
cost, and route cost [62]. Lim et al. focused on the improvement
of service quality while considering
price reduction due to the increase of online demand, volume of
delivery, and short-term responses,
as well as the lack of mid- and long-term responses due to
increase in online transactions [63]. Park et al.
investigated methods of increasing productivity while
considering both logistics and employees by
utilizing a wireless Internet system [64], while Kim and Choi
explored the effects of a corporation’s
logistics technology on courier services based on online
shopping malls as courier service users [65].](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-29-320.jpg)
![In summary, most previous research concerning the courier
service industry focused on the
analysis of courier service networks and delivery efficiency in
terms of optimal logistics structures,
Sustainability 2018, 10, 3778 6 of 15
methods for improving service quality, and minimization of
costs in terms of operational requirements.
Only a few case studies gathered and analyzed big data or BI
applications in the field, considering the
increase in e-commerce delivery demand.
3.2. Case Study: CJ Logistics
This study uses the case of CJ Logistics, Korea’s largest
logistics company. It examines the sorting
process, especially regarding decisions about loading/unloading
docks and hub terminals, which are
at the core of courier services, to examine the effective use of
big data/BDA through BI.
CJ Logistics was selected as the research subject as it is the
largest logistics service provider in
Korea with the highest market share and sales revenue of KRW
7110.3 billion in 2017 [66]. In addition,
as shown in Figure 1 (big data case of CJ Logistics, March
2018), the company is an innovation
leader in the industry. It is traditionally considered a 3D
business that uses BI based on high-tech
automation-oriented technology, engineering, and system and
solution plus consulting (TES + C),
while actively and rapidly adopting big data/BDA at the same
time.](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-30-320.jpg)
![Sustainability 2018, 10, x FOR PEER REVIEW 6 of 15
3.2. Case Study: CJ Logistics
This study uses the case of CJ Logistics, Korea’s largest
logistics company. It examines the sorting
process, especially regarding decisions about loading/unloading
docks and hub terminals, which are
at the core of courier services, to examine the effective use of
big data/BDA through BI.
CJ Logistics was selected as the research subject as it is the
largest logistics service provider in
Korea with the highest market share and sales revenue of KRW
7110.3 billion in 2017 [66]. In addition,
as shown in Figure 1 (big data case of CJ Logistics, March
2018), the company is an innovation leader
in the industry. It is traditionally considered a 3D business that
uses BI based on high-tech
automation-oriented technology, engineering, and system and
solution plus consulting (TES + C),
while actively and rapidly adopting big data/BDA at the same
time.
Figure 1. Technology, engineering, system and solution plus
consulting (TES + C) of CJ Logistics.
CJ Logistics is a market leader equipped with cutting-edge
logistics technologies, including real-
time tracking of freight, an integrated courier and freight
tracking system that enables users to view
customer information and requirements, satellite vehicle
tracking, and temperature control systems](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-31-320.jpg)
![[67]. In 2017, CJ Logistics invested more than KRW 120 billion
to automate its sorting process through
sub-terminals to aid sustainable growth. CJ Logistics’
infrastructure is more than three times bigger
than that of its closest competitor in the courier service
industry. With five hub terminals, more than
270 sub-terminals, and more than 16,000 vehicles, CJ Logistics
processes more than 5.3 million
packages per day. Its mega hub terminal in Gwangju, Gyeonggi-
do Province—which was due for
completion in August 2018 with an investment of more than
KRW 400 billion—will utilize
convergence technologies such as big data, robots, and IoT to
expand its services for the convenience
of its customers across Korea. This will include same-day
delivery, same-day return, and scheduled
delivery services. The company is simultaneously moving
forward with its planned international
growth. At the end of 2017, CJ Logistics had a global network
of 238 centers in 137 cities and 32
countries. It opened the Shenyang Flagship Center, a mammoth
logistics center in Shenyang, China,
on 15 June 2018. The purpose of this investment was to
accelerate the company’s business in northern
Asia, including three provinces of northeastern China—
Liaoning, Jilin, and Heilongjiang. The
company has implemented huge capital expenditure to broaden
its business efficiently, laying the
groundwork for sustainable growth and expansion by raising the
entrance barrier for rivals (big data
case of CJ Logistics, March 2018).
CJ Logistics mainly uses a hub-and-spoke system, which
connects points via hubs or logistics
centers dealing with massive cargo volumes in its courier
service; it also uses a point-to-point](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-32-320.jpg)
![operational system directly connecting origins and destinations.
The point-to-point system delivers
Figure 1. Technology, engineering, system and solution plus
consulting (TES + C) of CJ Logistics.
CJ Logistics is a market leader equipped with cutting-edge
logistics technologies,
including real-time tracking of freight, an integrated courier and
freight tracking system that enables
users to view customer information and requirements, satellite
vehicle tracking, and temperature
control systems [67]. In 2017, CJ Logistics invested more than
KRW 120 billion to automate its sorting
process through sub-terminals to aid sustainable growth. CJ
Logistics’ infrastructure is more than
three times bigger than that of its closest competitor in the
courier service industry. With five hub
terminals, more than 270 sub-terminals, and more than 16,000
vehicles, CJ Logistics processes more
than 5.3 million packages per day. Its mega hub terminal in
Gwangju, Gyeonggi-do Province—which
was due for completion in August 2018 with an investment of
more than KRW 400 billion—will
utilize convergence technologies such as big data, robots, and
IoT to expand its services for the
convenience of its customers across Korea. This will include
same-day delivery, same-day return,
and scheduled delivery services. The company is simultaneously
moving forward with its planned
international growth. At the end of 2017, CJ Logistics had a
global network of 238 centers in 137 cities
and 32 countries. It opened the Shenyang Flagship Center, a
mammoth logistics center in Shenyang,
China, on 15 June 2018. The purpose of this investment was to
accelerate the company’s business in](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-33-320.jpg)
![northern Asia, including three provinces of northeastern
China—Liaoning, Jilin, and Heilongjiang.
The company has implemented huge capital expenditure to
broaden its business efficiently, laying the
Sustainability 2018, 10, 3778 7 of 15
groundwork for sustainable growth and expansion by raising the
entrance barrier for rivals (big data
case of CJ Logistics, March 2018).
CJ Logistics mainly uses a hub-and-spoke system, which
connects points via hubs or logistics
centers dealing with massive cargo volumes in its courier
service; it also uses a point-to-point
operational system directly connecting origins and destinations.
The point-to-point system delivers
to and from terminals, saving time on package arrivals while
alleviating capacity issues during the
peak season. However, growing volumes may increase costs, as
they require more investment in
terminals; a volume imbalance among terminals can cause
unnecessary additional costs. On the other
hand, in the hub-and-spoke system, packages are gathered and
sorted in a large terminal before being
delivered to a destination terminal. The advantage of this
system is that it reduces arrival time to
the terminals, easing the imbalance in volume. However, the
disadvantages are that it may delay
deliveries to distant or rural areas during the peak season and
requires a large-scale hub terminal [67].
Since CJ Logistics mostly uses the hub-and-spoke system,
whose core is the logistics process](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-34-320.jpg)
![at the hub terminal, this study focuses on decisions concerning
the loading/unloading docks in the
process. This focus point was selected for the following
reasons. First, few previous studies have
focused on this segment, even though it has greater room for
improvement regarding productivity and
efficiency than other segments. Second, the importance of this
segment may have been overlooked,
since standardizing the process is challenging owing to
differences in the environment, such as the
distance between buildings or shape of the space. Third, there
are many other difficulties to address,
including outsourcing, warehouse management, freight payment,
inventory management, packing,
customs clearance, and customer claims [51]. Many courier
service providers allocate hub terminal
docks for loading/unloading simply according to terminal
conditions, such as the distance between
docks and number of packages, mostly based on past
experience. By contrast, CJ Logistics has
dramatically improved productivity and efficiency by “seeing
the unseen” through the use of big
data/BDA and promoting faster and better decision making
through BI.
The hub terminal process was selected from the three general
stages of courier services, namely,
pick-up, transport/sorting, and delivery (Figure 2). This process
was selected because it is the central
process connecting pick-ups from different locations with
delivery to different destinations [68,69].
Sustainability 2018, 10, x FOR PEER REVIEW 7 of 15
to and from terminals, saving time on package arrivals while](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-35-320.jpg)
![alleviating capacity issues during the
peak season. However, growing volumes may increase costs, as
they require more investment in
terminals; a volume imbalance among terminals can cause
unnecessary additional costs. On the other
hand, in the hub-and-spoke system, packages are gathered and
sorted in a large terminal before being
delivered to a destination terminal. The advantage of this
system is that it reduces arrival time to the
terminals, easing the imbalance in volume. However, the
disadvantages are that it may delay
deliveries to distant or rural areas during the peak season and
requires a large-scale hub terminal
[67].
Since CJ Logistics mostly uses the hub-and-spoke system,
whose core is the logistics process at
the hub terminal, this study focuses on decisions concerning the
loading/unloading docks in the
process. This focus point was selected for the following
reasons. First, few previous studies have
focused on this segment, even though it has greater room for
improvement regarding productivity
and efficiency than other segments. Second, the importance of
this segment may have been
overlooked, since standardizing the process is challenging
owing to differences in the environment,
such as the distance between buildings or shape of the space.
Third, there are many other difficulties
to address, including outsourcing, warehouse management,
freight payment, inventory
management, packing, customs clearance, and customer claims
[51]. Many courier service providers
allocate hub terminal docks for loading/unloading simply
according to terminal conditions, such as
the distance between docks and number of packages, mostly](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-36-320.jpg)
![based on past experience. By contrast,
CJ Logistics has dramatically improved productivity and
efficiency by “seeing the unseen” through
the use of big data/BDA and promoting faster and better
decision making through BI.
The hub terminal process was selected from the three general
stages of courier services, namely,
pick-up, transport/sorting, and delivery (Figure 2). This process
was selected because it is the central
process connecting pick-ups from different locations with
delivery to different destinations [68,69].
Figure 2. General courier service structure.
An incident that occurs at the hub terminal can have a serious
impact on the entire cycle—from
pick-up to delivery—and could cause a bottleneck effect at hub
terminals. This is a significant issue
that needs to be addressed to secure growth in the industry, as it
can paralyze transportation and
delivery within a company on a large scale. Resolving this issue
alongside difficulties in other areas
by using big data/BDA could improve company productivity and
efficiency as a whole.
Figure 2. General courier service structure.
Sustainability 2018, 10, 3778 8 of 15
An incident that occurs at the hub terminal can have a serious
impact on the entire cycle—from
pick-up to delivery—and could cause a bottleneck effect at hub](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-37-320.jpg)
![production of boxes for each customer, classification of
customers based on volume density, and etc.
which are based on practical business applications to build
integrated knowledge.
Author Contributions: Conceptualization, D.-H.J. and H.-J.K.;
methodology, D.-H.J.; software, D.-H.J.; validation,
D.-H.J. and H.-J.K.; formal analysis, D.-H.J.; investigation, D.-
H.J.; resources, D.-H.J.; data curation, D.-H.J.;
writing—original draft preparation, D.-H.J.; writing—review
and editing, D.-H.J. and H.-J.K.; visualization,
D.-H.J.; supervision, D.-H.J. and H.-J.K.; project
administration, D.-H.J. and H.-J.K.
Funding: This research received no external funding.
Conflicts of Interest: CJ Logistics provided some part of the
data for the case study to Dong Hui Jin and validated
all the data used in this study.
References
1. Laney, D. 3D Data Management: Controlling Data Volume,
Velocity and Variety; Application Delivery Strategy;
META Group: Stamford, CT, USA, 2001; Volume 949.
2. McAfee, A.; Brynjolfsson, E. Big data: The management
revolution. Harv. Bus. Rev. 2012, 90, 60–68. [PubMed]
3. Fosso Wamba, S.; Akter, S.; Edwards, A.; Chopin, G.;
Gnanzou, D. How ‘big data’ can make big impact:
Findings from a systematic review and a longitudinal case
study. Int. J. Prod. Econ. 2015, 165, 234–246.
[CrossRef]
4. Wang, Y.; Kung, L.; Wang, W.Y.C.; Cegielski, C.G. An](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-48-320.jpg)
![integrated big data analytics-enabled transformation
model: Application to health care. Inf. Manag. 2018, 55, 64–79.
[CrossRef]
5. White, M. Digital workplaces: Vision and reality. Bus. Inf.
Rev. 2012, 209, 205–214. [CrossRef]
6. Kambatla, K.; Kollias, G.; Kumar, V.; Grama, A. Trends in
big data analytics. J. Parallel Distrib. Comput. 2014,
74, 2561–2573. [CrossRef]
7. Addo-Tenkorang, R.; Helo, P.T. Big data applications in
operations/supply-chain management: A literature
review. Comput. Ind. Eng. 2016, 101, 528–543. [CrossRef]
8. Richey, R.G.; Morgan, T.R.; Lindsey-Hall, K.; Adams, F.G.
A global exploration of big data in the supply
chain. Int. J. Phys. Distrib. Logist. Manag. 2016, 46, 710–739.
[CrossRef]
9. Yu, W.; Chavez, R.; Jacobs, M.A.; Feng, M. Data-driven
supply chain capabilities and performance:
A resource-based view. Transp. Res. E Logist. 2018, 114, 371–
385. [CrossRef]
http://www.ncbi.nlm.nih.gov/pubmed/23074865
http://dx.doi.org/10.1016/j.ijpe.2014.12.031
http://dx.doi.org/10.1016/j.im.2017.04.001
http://dx.doi.org/10.1177/0266382112470412
http://dx.doi.org/10.1016/j.jpdc.2014.01.003
http://dx.doi.org/10.1016/j.cie.2016.09.023
http://dx.doi.org/10.1108/IJPDLM-05-2016-0134
http://dx.doi.org/10.1016/j.tre.2017.04.002
Sustainability 2018, 10, 3778 13 of 15](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-49-320.jpg)
![10. Roßmann, B.; Canzaniello, A.; Von der Gracht, H.;
Hartmann, E. The future and social impact of Big Data
Analytics in Supply Chain Management: Results from a Delphi
study. Technol. Forecast. Soc. Chang. 2018,
130, 135–149. [CrossRef]
11. Russom, P. Big Data Analytics. TDWI Best Practices
Report, Fourth Quarter. 2011. Available online: tdwi.org
(accessed on 12 July 2018).
12. Rouse, M. Big Data Analytics. 2012. Available online:
http://searchbusinessanalytics.techtarget.com/
definition/big-data-analytics (accessed on 12 July 2018).
13. LaValle, S.; Lesser, E.; Shockley, R.; Hopkins, M.S.;
Kruschwitz, N. Big data, analytics and the path from
insights to value. MIT Sloan Manag. Rev. 2013, 52, 21–31.
14. Loshin, D. Big Data Analytics: From Strategic Planning to
Enterprise Integration with Tools, Techniques, NoSQL,
and Graph; Elsevier: Waltham, MA, USA, 2013.
15. Tiwari, S.; Wee, H.M.; Daryanto, Y. Big data analytics in
supply chain management between 2010 and 2016:
Insights to industries. Comput. Ind. Eng. 2018, 115, 319–330.
[CrossRef]
16. Gilad, B.; Herring, J.P. The Art and Science of Business
Intelligence Analysis; JAI Press Ltd.: Greenwich, UK, 1996.
17. Davenport, T.; Prusak, L. Working Knowledge; HBS Press:
Boston, MA, USA, 1998.
18. Berson, A.; Smith, S.; Thearling, K. Building Data Mining
Application for CRM; McGraw-Hill: New York, NY,
USA, 2000.](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-50-320.jpg)
![19. Simon, A.; Shaffer, S. Data Warehousing and Business
Intelligence for E-Commerce; Morgan Kaufmann Publishers:
San Francisco, CA, USA, 2001.
20. Solomon, N. Business intelligence. Commun. Assoc. Inf.
Syst. 2004, 13, 177–195.
21. Fan, S.; Raymond, Y.K.; Lau, J.; Zhaob, L. Demystifying
big data analytics for business intelligence through
the lens of marketing mix. Big Data Res. 2015, 2, 28–32.
[CrossRef]
22. Manyika, J.; Chui, M.; Brown, B.; Bughin, J.; Dobbs, R.;
Roxburgh, C.; Byers, A.H. Big Data: The Next Frontier
for Innovation, Competition, and Productivity; McKinsey
Global Institute: Washington, DC, USA, 2011.
23. Kang, M.; Kim, S.; Park, S. Analysis and utilization of big
data. J. Inf. Sci. Soc. 2012, 30, 25–32.
24. Liang, T.; Liu, Y. Research landscape of business
intelligence and big data analytics: A bibliometrics study.
Expert Syst. Appl. 2018, 111, 2–10. [CrossRef]
25. Tankard, C. Big data security. Netw. Secur. 2012, 7, 5–8.
[CrossRef]
26. Ram, J.; Zhang, C.; Koronios, A. The implications of big
data analytics on business intelligence: A qualitative
study in China. Procedia Comput. Sci. 2016, 87, 221–226.
[CrossRef]
27. Wang, L.; Alexander, C.A. Big data driven supply chain
management and business administration. Am. J.
Econ. Bus. Adm. 2015, 7, 60–67. [CrossRef]
28. Vera-Baquero, A.; Palacios, R.C.; Stantchev, V.; Molloy, O.
Leveraging big-data for business process analytics.](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-51-320.jpg)
![Learn. Organ. 2015, 22, 215–228. [CrossRef]
29. Tan, K.H.; Zhan, Y.Z.; Ji, G.; Ye, F.; Chang, C. Harvesting
big data to enhance supply chain innovation
capabilities: An analytic infrastructure based on deduction
graph. Int. J. Prod. Econ. 2015, 165, 223–233.
[CrossRef]
30. Miškuf, M.; Zolotová, I. Application of Business
Intelligence
Solution
s on Manufacturing Data.
In Proceedings of the 13th International Symposium on Applied
Machine Intelligence and Informatics,
Herl’any, Slovakia, 22–24 January 2015.
31. Luhn, H.P. A business intelligence system. IBM J. Res.
Dev. 1958, 2, 314–319. [CrossRef]
32. Vitt, E.; Luckevich, M.; Misner, S. Business Intelligence;
Microsoft Press: Redmond, WA, USA, 2002.
33. Turban, E.; Aronson, J.E.; Liang, T.P. Decision Support and
Intelligence Systems; Prentice-Hall: Upper Saddle
River, NJ, USA, 2005.
34. Van-Hau, T. Getting value from business intelligence](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-52-320.jpg)
![systems: A review and research agenda.
Decis. Support Syst. 2017, 93, 111–124.
35. Turban, E.; Volonino, L. Information Technology for
Management, 7th ed.; John Wiley & Sons, Inc.: New York,
NY, USA, 2010.
36. Larsona, D.; Chang, V. A review and future direction of
agile, business intelligence, analytics and data
science. Int. J. Inf. Manag. 2016, 36, 700–710. [CrossRef]
37. Chen, H.; Chiang, R.H.; Storey, V.C. Business intelligence
and analytics: From big data to big impact. MIS Q.
2012, 36, 1165–1188. [CrossRef]
http://dx.doi.org/10.1016/j.techfore.2017.10.005
tdwi.org
http://searchbusinessanalytics.techtarget.com/definition/big-
data-analytics
http://searchbusinessanalytics.techtarget.com/definition/big-
data-analytics
http://dx.doi.org/10.1016/j.cie.2017.11.017
http://dx.doi.org/10.1016/j.bdr.2015.02.006
http://dx.doi.org/10.1016/j.eswa.2018.05.018](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-53-320.jpg)
![http://dx.doi.org/10.1016/S1353-4858(12)70063-6
http://dx.doi.org/10.1016/j.procs.2016.05.152
http://dx.doi.org/10.3844/ajebasp.2015.60.67
http://dx.doi.org/10.1108/TLO-05-2014-0023
http://dx.doi.org/10.1016/j.ijpe.2014.12.034
http://dx.doi.org/10.1147/rd.24.0314
http://dx.doi.org/10.1016/j.ijinfomgt.2016.04.013
http://dx.doi.org/10.2307/41703503
Sustainability 2018, 10, 3778 14 of 15
38. Wixom, B.; Ariyachandra, T.; Douglas, D.; Goul, M.;
Gupta, B.; Iyer, L.; Kulkarni, U.; Mooney, J.G.;
Phillips-Wren, G.; Turetken, O. The current state of business
intelligence in academia: The arrival of
big data. Commun. Assoc. Inf. Syst. 2014, 34, 1–13.
39. Bala, M.; Balachandran, S.P. Challenges and benefits of
deploying big data analytics in the cloud for business
intelligence. Procedia Comput. Sci. 2017, 112, 1112–1122.
40. Davenport, T.H. How strategists use ‘big data’ to support
internal business decisions, discovery and
production. Strat. Leadersh. 2014, 42, 45–50. [CrossRef]](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-54-320.jpg)
![41. Narayanan, V. Using big-data analytics to manage data
deluge and unlock real-time business insights.
J. Equip. Lease Financ. 2014, 32, 1–7.
42. Erevelles, S.; Fukawa, N.; Swayne, L. Big data consumer
analytics and the transformation of marketing.
J. Bus. Res. 2016, 69, 897–904. [CrossRef]
43. Lazer, D.; Kennedy, R.; King, G.; Vespignani, A. The
parable of Google flu: Traps in big data analysis. Science
2014, 343, 1203–1205. [CrossRef] [PubMed]
44. Zhong, R.Y.; Huang, G.Q.; Lan, S.; Dai, Q.Y.; Chen, X.;
Zhang, T. A big data approach for logistics trajectory
discovery from RFID-enabled production data. Int. J. Prod.
Econ. 2015, 165, 260–272. [CrossRef]
45. Marcos, D.; Assunção, R.N.; Calheiros, S.B.; Marco, A.S.;
Netto, R.B. Big data computing and clouds:
Trends and future directions. J. Parallel Distrib. Comput. 2015,
79, 3–15.
46. Kim, Y. Enterprise innovation through the introduction of
big data-based advanced analysis system: Case and](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-55-320.jpg)
![methodology. IE Mag. 2013, 20, 43–49.
47. Wang, G.; Gunasekaran, A.; Ngai, E.W.T.; Papadopoulos, T.
Big data analytics in logistics and supply chain
management: Certain investigations for research and
applications. Int. J. Prod. Econ. 2016, 176, 98–110.
[CrossRef]
48. Queiroz, M.M.; Telles, R. Big data analytics in supply chain
and logistics: An empirical approach. Int. J.
Logist. Manag. 2018, 29, 767–783. [CrossRef]
49. Hopkins, J.; Hawking, P. Big data analytics and IoT in
logistics: A case study. Int. J. Logist. Manag. 2018, 29,
575–591. [CrossRef]
50. Korean Statistical Information Service. 2018. Available
online: http://kostat.go.kr/portal/korea/index.
action (accessed on 18 October 2018).
51. Jo, Y.; Yoon, M. Analysis of courier service market of
South Korea. Korea Technol. Innov. Soc. 2001, 245–270.
52. Ho, J.S.Y.; Teik, D.O.L.; Tiffany, F.; Kok, L.F.; The, T.Y.
Logistic Service Quality among Courier Services in](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-56-320.jpg)
![Malaysia. In International Conference on Economics, Business
Innovation, IPEDR; IACSIT Press: Singapore, 2012;
Volume 38, pp. 113–117.
53. Lee, C. A study on the strengthening competitiveness of
railway logistics business with the growth of the
courier business. J. Vocat. Rehabil. 2002, 25, 75–95.
54. Park, Y. A study on the domestic express courier service’s
present situation and further theme in the era of
e-commerce. Korean J. Bus. Adm. 2003, 39, 1425–1444.
55. Jung, J.; Kim, C. A Study on Quality Improvement of
Courier Service Using Quality Function Deployment.
In Proceedings of the Conference of the Korean Society of
Business Administration, Seoul, Korea, 24–25
November 2015; pp. 451–468.
56. Davis, F.D. Perceived usefulness, perceived ease of use, and
user acceptance of information technology.
MIS Q. 1989, 13, 361–391. [CrossRef]
57. DeLone, W.H.; McLean, E.R. Information system success:
The quest for the dependent variable. Inf. Syst. Res.
1992, 3, 60–92. [CrossRef]](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-57-320.jpg)
![Voice Detection (LVD) technique with biometric features such
as face and voice. IPSs updated with new LVD features
protect the VoIP services not only from attacks but also from
misuses.
A Comprehensive Survey of Security Issues and
Defense Framework for VoIP Cloud
Ashutosh Satapathy* and L. M. Jenila Livingston
School of Computing Science and Engineering, VIT University,
Chennai - 600127, Tamil Nadu, India;
[email protected], [email protected]
Keywords: Defense Mechanisms, Liveness Voice Detection,
VoIP Cloud, Voice over Internet Protocol, VoIP Security Issues
1. Introduction
The rapid progress of VoIP over traditional services is
led to a situation that is common to many innovations
and new technologies such as VoIP cloud and peer to
peer services like Skype, Google Hangout etc. VoIP is the
technology that supports sending voice (and video) over
an Internet protocol-based network1,2. This is completely
different than the public circuit-switched telephone net-
work. Circuit switching network allocates resources to](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-66-320.jpg)
![36(1):16–24.
43. Audiopedia. Honeypot (computing). Available from:
https://www.youtube.com/watch?v=2fXAw33jOBk. [Cited 2014
Dec].
44. Goel R, Sardana A, Joshi RC. Wireless honeypot:
framework, architectures and tools.
International Journal of Network Security. 2013; 15(5):373–83.
45. Li Z, Grochulla M, Thormahlen T. Multiple active speaker
localization based on audio-
visual fusion in two stages. Proceedings IEEE International
Conference on Multisensor
Fusion Integration Intelligence Systems (MFI); Hamburg:
Germany; 2012. p. 262–68.
46. Zhu ZY, He QH, Feng XH, Xiongli Y, Wang ZF. Liveness
detection using time drift
between lip movement and voice. Proceedings IEEE
International Conference on Machine
Learning Cybernetics (ICMLC); Tianjin: China; 2013. p. 973–
78.
47. Chetty G. Biometric liveness detection based on cross modal](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-71-320.jpg)
![Aug].
7. Butcher D, Li X, Guo J. Security challenge and defense
in VoIP infrastructures. IEEE Transactions on Systems
Man and Cybernetics Part C: Applications Reviews. 2007;
37(6):1152–62.
8. Graves K. Certified ethical hacker study guide, 4th ed.
Wiley: Danvers, MA; 2010.
9. Sawada H, Higashimoto T. A mechanical voice system
and its adaptive learning for the mimicry of human vocal-
ization. Proceedings IEEE International Symposium on
Computational Intelligence Robotics and Automation;
Cobe, Japan; 2003. p. 1040–45.
10. Hatkar AA, Varade GA, Hatkar AP. Media access control
spoofing techniques and counter measures. International
Journal Scientific & Engineering Research. 2012; 2(6):1–5 .
11. Nam SY, Kim D, Kim J. Enhanced ARP: preventing ARP
poisoning-based Man-in-the-Middle Attacks. IEEE
Communications Letters. 2010; 14(2):187–9.
12. Ma Y. An effective method for defense against IP spoof-](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-124-320.jpg)
![22. Fu D, Shi F. Buffer overflow exploit and defensive tech-
niques. IEEE International Conference on Multimedia
Information Networking and Security (MINES); Nanjing,
China; 2012. p. 87–90.
23. Ransome JF, Rittinghouse JR. VoIP security risks. VoIP
Security, Casey E, editor; Elsevier: Burlington, MA; 2005.
24. Hardening the operating system. Available from: http://cdn.
ttgtmedia.com/searchEnterpriseLinux/downloads/466_
HTC_Linux_02.pdf. [Citied 2014 Oct].
25. Filiol E. Taxonomy, techniques and tools. Computer
Viruses: From Theory to Applications, 1st ed.; Springer:
Verlag, France; 2004.
26. Shar LK, Tan HBK. Defeating SQL injection. IEEE
Computer:
Gender Diversity in Computing. 2013; 46(3):69–77.
27. Natan RB. Application security. Implementing Database
Security and Auditing; Elsevier: Burlington, MA; 2005.
28. Mohamed NN, Mashim H, Yussoff YM. Compression
and encryption technique on securing TFTP packet.](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-127-320.jpg)
![Ashutosh Satapathy and L. M. Jenila Livingston
Indian Journal of Science and Technology 13Vol 9 (6) |
February 2016 | www.indjst.org
34. IEEE 802.1X port-based authentication, CISCO. Available
from: http://www.cisco.com/c/en/us/td/docs/switches/lan/
catalyst6500/ios/122SX/configuration/guide/book/dot1x.
html#wp1133592. [Cited 2015 Jan].
35. deGraaf R, Aycock J, Jacobson M. Improved port knocking
with strong authentication. Proceeding. IEEE 21st Annual
Computer Security Applications Conference (ACSAC);
Tucson, Arizona; 2005. p. 453–62.
36. Sehgal PK, Nath R. An encryption based dynamic and
secure
routing protocol for mobile Ad-hoc network. International
Journal of Computer Science Security. 2009; 3(1):16–22.
37. Chacon S, Benhaddou D, Gurkan D. Secure Voice over
Internet Protocol (VoIP) using Virtual Private Networks](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-129-320.jpg)
![https://www.youtube.com/watch?v=2fXAw33jOBk. [Cited
2014 Dec].
44. Goel R, Sardana A, Joshi RC. Wireless honeypot: frame-
work, architectures and tools. International Journal of
Network Security. 2013; 15(5):373–83.
45. Li Z, Grochulla M, Thormahlen T. Multiple active speaker
localization based on audio-visual fusion in two stages.
Proceedings IEEE International Conference on Multisensor
Fusion Integration Intelligence Systems (MFI); Hamburg:
Germany; 2012. p. 262–68.
46. Zhu ZY, He QH, Feng XH, Xiongli Y, Wang ZF. Liveness
detection using time drift between lip movement and voice.
Proceedings IEEE International Conference on Machine
Learning Cybernetics (ICMLC); Tianjin: China; 2013.
p. 973–78.
47. Chetty G. Biometric liveness detection based on cross modal
fusion. IEEE 12th International Conference on Information
Fusion (FUSION). Seattle: WA; 2009. p. 2255–62.](https://image.slidesharecdn.com/classthecosoframeworkofinternalcontrolsispracticed-221025205601-47bb5df1/85/Class-The-COSO-framework-of-internal-controls-is-practiced-docx-131-320.jpg)
Class, The COSO framework of internal controls is practiced .docx
- 1. Class, The COSO framework of internal controls is practiced within companies around the world. The objectives of the COSO framework are closely related to its five components. For this week’s activity, please discuss these five components of the COSO framework. Be sure to include each components’ impact on each of the COSO framework objectives. What do you feel an auditor would most be concerned with during an IT audit? Lastly, discuss suggestions for integrating COSO framework compliance into a company in which you are familiar. Your paper should meet the following requirements: • Be approximately 2-4 pages in length, not including the required cover page and reference page. • Follow APA6 guidelines. Your paper should include an introduction, a body with fully developed content, and a conclusion. • Support your answers with the readings from the course and at least two scholarly journal articles to support your positions, claims, and observations, in addition to your textbook. The UC Library is a great place to find resources. • Be clearly and well-written, concise, and logical, using excellent grammar and style techniques. You are being graded in part on the quality of your writing. Managing and Using Information Systems: A Strategic Approach – Sixth Edition Keri Pearlson, Carol Saunders, and Dennis Galletta © Copyright 2016
- 2. John Wiley & Sons, Inc. Chapter 12 Knowledge Management, Business Intelligence, and Analytics 2 Opening Case: Netflix What gave Netflix assurance that House of Cards would be a success? What gives Netflix a competitive advantage? © 2016 John Wiley & Sons, Inc. 3 Data from 33 million people provided strong evidence that the British version was very popular and Kevin Spacey was also a popular actor Again, data from millions of users, showing what they watch, when they pause, when they rewind, etc. Patterns emerge that speak volumes about preferences. Their analytics algorithms
- 3. provide better data than focus groups (and in real time). 3 More Real World Examples Caesar’s and Capital One both collect and analyze customer data. Result: They can determine who are the most profitable customers and then follow up with them. Caesar’s: frequent gamblers Capital One: charge a lot and pay off slowly They provide products that would appeal to the profitable customers. © 2016 John Wiley & Sons, Inc. 4 4 A Real World Example from Sports Oakland As and Boston Red Sox baseball teams Crunched the numbers on the potential players, such as on-base percentage Others who did not do the analysis failed to recognize the talent © 2016 John Wiley & Sons, Inc. 5
- 4. 5 Five Ways Data Analytics can Help an Organization (McKinsey and Co.) Making data more transparent and usable more quickly Exposing variability and boosting performance Tailoring products and services Improving decision-making Improving products © 2016 John Wiley & Sons, Inc. 6 Terminology Knowledge management: The processes needed to generate, capture, codify and transfer knowledge across the organization to achieve competitive advantage Business intelligence: The set of technologies and processes that use data to understand and analyze business performance Business analytics: The use of quantitative and predictive models, algorithms, and evidence-based management to drive decisions © 2016 John Wiley & Sons, Inc. 7
- 5. Data, Information, and Knowledge (reprise) © 2016 John Wiley & Sons, Inc. 8 8 The Value of Managing KnowledgeValueSources of ValueSharing best practicesAvoid reinventing the wheel Build on valuable work and expertiseSustainable competitive advantageShorten innovation life cycle Promote long term results and returnsManaging overloadFilter data to find relevant knowledge Organize and store for easy retrievalRapid changeBuild on/customize previous work for agility Streamline and build dynamic processes Quick response to changesEmbedded knowledge from productsSmart products can gather information Blur distinction between manufacturing/service Add value to productsGlobalizationDecrease cycle times by sharing knowledge globally Manage global competitive pressures Adapt to local conditionsInsurance for downsizingProtect against loss of knowledge when departures occur Provide portability for workers who change roles Reduce time to acquire knowledge © 2016 John Wiley & Sons, Inc. 9
- 6. Dimensions of Knowledge Explicit Teachable Articulable Observable in use Scripted Simple Documented Tacit Not teachable Not articulable Not observable Rich Complex Undocumented Examples: Estimating work Deciding best action Examples: Explicit steps Procedure manuals © 2016 John Wiley & Sons, Inc. 10 10
- 7. Four Modes of Knowledge Conversion (and examples) Transferring by mentoring, apprenticeship Transferring by models, metaphors Learning by doing; studying manuals Obtaining and following manuals © 2016 John Wiley & Sons, Inc. 11 Knowledge Management – Four Processes Generate – discover “new” knowledge Capture – scan, organize, and package it Codify – represent it for easy access and transfer (even as simple as using hash tags to create a folksonomy) Transfer – transmit it from one person to another to absorb it © 2016 John Wiley & Sons, Inc. 12 12
- 8. Measures of KM Project Success Example of specific benefits of a KM project: Enhanced effectiveness Revenue generated from extant knowledge assets Increased value of extant products and services Increased organizational adaptability More efficient re-use of knowledge assets Reduced costs Reduced cycle time © 2016 John Wiley & Sons, Inc. 13 13 Components of Business Analytics ComponentDefinitionExampleData SourcesData streams and repositories Data warehouses; weather dataSoftware ToolsApplications and processes for statistical analysis, forecasting, predictive modeling, and optimizationData mining process; forecasting software packageData-Driven EnvironmentOrganizational environment that creates and sustains the use of analytics toolsReward system that encourages the use of the analytics tools; willingness to test or experimentSkilled WorkforceWorkforce that has the training, experience, and capability to use the analytics toolsData scientists, chief data officers, chief analytics officers, analysts, etc. Netflix, Caesars and Capital One have these skills © 2016 John Wiley & Sons, Inc. 14
- 9. 14 Data Sources for Analytics Structured (customers, weather patterns) or unstructured (Tweets, YouTube videos) Internal or external Data warehouses full of a variety of information Real-time information such as stock market prices © 2016 John Wiley & Sons, Inc. 15 Data Mining Combing through massive amounts of customer data, usually focused on: Buying patterns/habits (for cross-selling) Preferences (to help identify new products/ features/enhancements to products) Unusual purchases (spotting theft) It also identifies previously unknown relationships among data. Complex statistics can uncover clusters on many dimensions not known previously (e.g., People who like movie x also like movie y) © 2016 John Wiley & Sons, Inc.
- 10. 16 Four Categories of Data Mining Tools Statistical analysis: Answers questions such as “Why is this happening?” Forecasting/Extrapolation: Answers questions such as “What if these trends continue?” Predictive modeling: Answers questions such as “What will happen next?” Optimization: Answers questions such as “What is the best that can happen?” © 2016 John Wiley & Sons, Inc. 17 How to be Successful Achieve a data driven culture Develop skills for data mining Use a Chief Analytics Officer (CAO) or Chief Data Officer (CDO) Shoot for high maturity level (see next slide) © 2016 John Wiley & Sons, Inc. 18
- 11. LevelDescriptionSource of Business Value1 – Reporting What happened?Reduce costs of summarizing, printing2 – Analyzing Why did it happen?Understanding root causes3 – DescribingWhat is happening nowReal-time understanding & corrective action4 – Predicting What will happen?Can take best action5 – PrescribingHow should we respond?Dynamic correction Five Maturity Levels of Analytical Capabilities © 2016 John Wiley & Sons, Inc. 19 BI and Competitive Advantage There is a very large amount of data in databases. Big data: techniques and technologies that make it economical to deal with very large datasets at the extreme end of the scale: e.g., 1021 data items Large datasets can uncover potential trends and causal issues Specialized computers and tools are needed to mine the data. Big data emerged because of the rich, unstructured data streams that are created by social IT. © 2016 John Wiley & Sons, Inc. 20
- 12. Practical Example Asthma outbreaks can be predicted by U. of Arizona researchers with 70% accuracy They examine tweets and Google searches for words and phrases like “wheezing” “sneezing” “inhaler” “can’t breathe” Relatively rare words (1% of tweets) but 15,000/day They examine the context of the words: “It was so romantic I couldn’t catch my breath” vs “After a run I couldn’t catch my breath” Helps hospitals make work scheduling decisions © 2016 John Wiley & Sons, Inc. 21 Sentiment Analysis Can analyze tweets and Facebook likes for Real-time customer reactions to products Spotting trends in reactions Useful for politicians, advertisers, software versions, sales opportunities © 2016 John Wiley & Sons, Inc. 22 Google Analytics and Salesforce.com
- 13. Listening to the community: Identifying and monitoring all conversations in the social Web on a particular topic or brand. Learning who is in the community: Identifying demographics such as age, gender, location, and other trends to foster closer relationships. Engaging people in the community: Communicating directly with customers on social platforms such as Facebook, YouTube, LinkedIn, and Twitter using a single app. Tracking what is being said: Measuring and tracking demographics, conversations, sentiment, status, and customer voice using a dashboard and other reporting tools. Building an audience: Using algorithms to analyze data from internal and external sources to understand customer attributes, behaviors, and profiles, then to find new similar customers © 2016 John Wiley & Sons, Inc. 23 Google Analytics Web site testing and optimizing: Understanding traffic to Web sites and optimizing a site’s content and design for increasing traffic. Search optimization: Understanding how Google sees an organization’s Web site, how other sites link to it, and how specific search queries drive traffic to it. Search term interest and insights: Understanding interests in particular search terms globally, as well as regionally, top searches for similar terms, and popularity over time. Advertising support and management: Identifying the best ways to spend advertising resources for online media. © 2016 John Wiley & Sons, Inc. 24
- 14. Internet of Things (IoT) Much big data comes from IoT Sensor data in products can allow the products to: Call for service (elevators, heart monitors) Parallel park, identify location/speed (cars) Alert you to the age of food (refrigerator) Waters the lawn when soil is dry (sprinklers) Self-driving cars find best route (Google) © 2016 John Wiley & Sons, Inc. 25 Intellectual Capital vs Intellectual Property Intellectual Capital: the process for managing knowledge Intellectual Property: the outputs; the desired product for the process Intellectual Property rights differ remarkably by country © 2016 John Wiley & Sons, Inc. 26 Closing Caveats
- 15. These are emerging concepts and disciplines Sometimes knowledge should remain hidden (tacit) for protection We should remain focused on future events, not just look over the past A supportive culture is needed in a firm to enable effective KM and BI © 2016 John Wiley & Sons, Inc. 27 Managing and Using Information Systems: A Strategic Approach – Sixth Edition Keri Pearlson, Carol Saunders, and Dennis Galletta © Copyright 2016 John Wiley & Sons, Inc. sustainability
- 16. Case Report Integrated Understanding of Big Data, Big Data Analysis, and Business Intelligence: A Case Study of Logistics Dong-Hui Jin and Hyun-Jung Kim * Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-70-7012-2722 Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018 ���������� ������� Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure competitiveness for sustainable growth. The rapid development of information and communication technology has made collection and analysis of big data essential, resulting in a considerable increase in academic studies on big data and big data analysis (BDA). However, many of these studies are not linked to BI, as companies do not understand and utilize the concepts in an integrated way. Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data, and BDA to show that they are not separate methods but an integrated decision support system. Second, we explore how businesses use big data and BDA practically in conjunction with BI through a case study of sorting and logistics processing of a typical courier enterprise. We focus on the company’s cost efficiency as regards to data collection, data
- 17. analysis/simulation, and the results from actual application. Our findings may enable companies to achieve management efficiency by utilizing big data through efficient BI without investing in additional infrastructure. It could also give them indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness. Keywords: business application; big data; big data analysis; business intelligence; logistics; courier service 1. Introduction A growing number of corporations depend on various and continuously evolving methods of extracting valuable information through big data and big data analysis (BDA) for business intelligence (BI) to make better decisions. The term “big data” refers to large amounts of information or data at a certain point in time and within a particular scope. However, big data have a short lifecycle with rapidly decreasing effective value, which makes it difficult for academic research to keep up with their fast pace. In addition, big data have no limits regarding their type, form, or scale, and their scope is too vast to narrow them down to a specific area of study. Big data can also simply refer to a huge amount of complex data, but their type, characteristics, scale, quality, and depth vary depending on the capabilities and purpose of each company. The same holds for the reliability and usability of the results gathered from analysis of the data. Previous studies generally agree on three main properties that define big data, namely, volume,
- 18. velocity, and variety, or the “3Vs” [1–4], which have recently been expanded to “5Vs” with the addition of veracity/verification and value [5–10]. There are numerous multi-dimensional methods for choosing how much data to gather and how to analyze and utilize the data. In brief, the methodology for extracting valuable information and taking full advantage of it could be more important than the data’s quality and quantity. A substantial amount of research has been devoted to establishing and developing theories concerning big data, Sustainability 2018, 10, 3778; doi:10.3390/su10103778 www.mdpi.com/journal/sustainability http://www.mdpi.com/journal/sustainability http://www.mdpi.com https://orcid.org/0000-0003-3698-4665 http://www.mdpi.com/2071- 1050/10/10/3778?type=check_update&version=1 http://dx.doi.org/10.3390/su10103778 http://www.mdpi.com/journal/sustainability Sustainability 2018, 10, 3778 2 of 15 BDA, and BI to address this need, but it is still challenging for a company to find, understand, integrate, and use the findings of these studies, which are often conducted independently and cover only select aspects of the subject. BDA refers to the overall process of applying advanced analytic skills, such as data mining, statistical analysis, and predictive analysis, to identify patterns,
- 19. correlations, trends, and other useful techniques [11–15]. BDA contributes to increasing the operational efficiency and business profits, and is becoming essential to businesses as big data spreads and grows rapidly. BI is a decision support system that includes the overall process of gathering extensive data, extracting useful data, and providing analytical applications. In general, BI has three common technological elements: a data warehouse integrating an online transaction processing system; a database addressing specific topics; online analytical processing that is used to analyze data in multi-dimensions in order to use those data; and data mining, which involves a series of technological methods for extracting useful knowledge from the gathered data [16–20]. Some areas of BI and BDA, such as data analysis and data mining, overlap. This is to be expected, as the raw data in BI have recently expanded to become big data in volume and scope. This has necessitated reorganization of the field and concepts of BI to provide business insights and enable better decision making based on BDA [21]. Although BI and BDA are generally studied independently, it is challenging and often unnecessary to distinguish between the two concepts when performing business tasks. Given the cost of gathering and analyzing big data, it is important to identify what data to collect, the range of the data, and the most cost-effective purpose of the data using BI. For this purpose, it is effective to understand and apply the methodology based on
- 20. experiences of companies shared through a case study. Therefore, the present study has the following aims. First, we explore the meaning of BI, big data, and BDA through a literature review and show that they are not separate methods, but rather an organically connected and integrated decision support system. Second, we use a case study to examine how big data and BDA are applied in practice through BI for greater understanding of the topic. The case study is conducted on a large and rapidly growing courier service in the logistics industry, which has a long history of research. In particular, we examine how the company efficiently allocates vehicles in hub terminals by collecting, analyzing, and applying big data to make informed decisions quickly, as well as uses BI to enhance productivity and cost-effectiveness. The rest of the paper proceeds as follows. Section 2 reviews the research background and literature related to BI, big data, and BDA. Section 3 presents the case study for the company and industry and discusses the case in detail. Finally, Section 4 concludes by discussing the implications and directions for future research. 2. Literature Review Big data have become a subject of growing importance, especially since Manyika et al. pointed out that they should be regarded as a key factor to increase corporate productivity and competitiveness [22]. Many researchers have shown interest in big data, as the rapid development of information and communication technology (ICT) generates a significant amount of data. This has led to lively
- 21. discussions about the collection, storage, and application of such data. In 2012, Kang et al. argued that the value of big data lies in making forecasts by recognizing situations, creating new value, simulating different scenarios, and analyzing patterns through analysis of the data on a massive scale [23]. In 2011, only 38 studies related to big data and BDA were listed in the Science Citation Index Expanded (SCIE), Social Science Citation Index (SSCI), Arts & Humanities Citation Index (AHCI), and Emerging Sources Citation Index (ESCI), but in 2012, this number increased to 92, and then rapidly increased to 1009 in 2015 and 3890 in 2017 [24]. Sustainability 2018, 10, 3778 3 of 15 2.1. Toward an Integrated Understanding of Big Data, BDA, and BI The research boom regarding big data has led to the development of BDA, through which valuable information is extracted from a company’s data. Companies are well aware of the increasing importance and investment need for BDA, as shown by Tankard [25], who claimed that a company can secure higher market share than its rivals and has the potential to increase its operating profit margin ratio by up to 60% by using big data effectively [25,26]. In the logistics industry, big data are used more widely than ever for supporting and optimizing operational processes, including supply chain management. Big data have been instrumental in developing new products and services, planning supply, managing inventory and risks, and providing customized
- 22. services [26–29]. BI has a longer history of research than that of big data. In 1865, Richard Millar Devens mentioned the concept in the Cyclopaedia of Commercial and Business Anecdotes [30], after which Luhn began using it in its modern meaning in 1958 [31]. Thereafter, Vitt et al. defined BI as an information system and method for decision making that incorporates the four-step cycle of analysis, insight, action, and performance measurement [32]. Solomon suggested a framework of BI and argued that research in the area was necessary [20]. Then, Turban et al. [33] expanded the scope of research to embrace data mining, warehousing and acquisition, and business analysis, and a growing number of studies followed. Miškuf and Zolotová studied BI using Cognos—a BI solution system adopted by IBM—and the case of U.S. Steel to ascertain how to best apply enterprise/manufacturing intelligence to manage manufacturing data efficiently [30]. Van-Hau pointed out the lack of a general framework in BI that would allow businesses to integrate results and systematically use them, as well as discussed issues that needed to be researched further [34]. In summary, the concept of BI has been expanding with regard to application systems and technologies that support enterprises in making better choices by gathering, storing, analyzing, and accessing data more effectively [35]. Previous research has dealt mostly with management and decision support systems and applications in BI, as well as technological aspects such as algorithms and computing for big data and BDA. However, the research areas are broadening, and
- 23. topics are becoming more diverse based on different macroeconomic environments, pace of technological progress, and division of the research field. Therefore, many studies on BI, big data, and BDA have been conducted separately. More importantly, big data research has a relatively short history, as it only started attracting significant attention since around 2012, when rapid development of ICTs led to discussions on how to gather and use the unprecedented amount of data generated. On the other hand, BI has long been a point of interest among researchers. The boundaries between these concepts—big data, BDA, and BI—are often unclear and ambiguous for companies. Generally, BI consists of an information value chain for gathering raw data, turning these data into useful information, management decision making, driving business results, and raising corporate value [36]. However, considering that “raw data” have been expanded to “big data” owing to the development of ICT and data storage, it is safe to say that BI and big data/BDA are presently not independent methods but organically coexist as an integrated decision support system, incorporating all processes from data gathering to management decision making in business. As research interest in big data began to grow since 2012, Chen et al. grouped previous works in the literature into BI and analytics and divided the evolution process of the subject into stages to examine the main characteristics and features of each stage [37]. Subsequently, Wixom et al. proposed the necessity of studying BI—including big data/BDA—and business analytics to address changes
- 24. in the field, since there was increasing awareness about the use and need of big data after the BI Conference of the Communications of the Association for Information Systems in 2009 and 2010 [38]. Fan et al. studied BI in the marketing sector in a big data environment and concluded that big data and BDA are disruptive technologies that reorganize the processes of BI to gain business insights for better decision making [21]. In addition, Bala and Balachandran defined cloud computing and big data as the two of the most important technologies in recent years and explored the improvement of decision-making processes through BI by integrating these two key technologies for storing and Sustainability 2018, 10, 3778 4 of 15 distributing data using cloud computing [39]. These cases illustrate that an increasing number of researchers are approaching BI and big data/BDA as an integrated concept. 2.2. In-Depth Research through Case Studies The growing interest in big data/BDA and rapid development in this area have strengthened BI as a decision support system, thereby promoting corporate management and enhancing business value by providing more valuable information to generate innovative ideas for new products and services. This has led to a rise in customer satisfaction, improved inventory and risk management, improved supply chain risk management, creation of competitive information, and provision of
- 25. real-time business insights [26–29,40–42]. Considering the short lifecycle of big data and their use in companies, there are numerous, multi-dimensional methods for deciding how much data to gather and how to analyze and utilize the data speedily and effectively. As David et al. emphasized in The Parable of Google Flu: Traps in Big Data Analysis, the essential element is turning data into valuable information, not the quantity of data or new data itself [43]. It is thus important to establish a database of integrated convergent knowledge and continue to develop this by accumulating knowledge and experiences through case studies based on practical use that apply the principals of BI and big data/BDA effectively. Below, we list examples of successful studies on the use and application of big data/BDA in practice. • Zhong et al. examined a big data approach that facilitates several innovations that can guide end-users to implement associated decisions through radio frequency identification (RFID) to support logistics management with RFID-Cuboids, map tables, and a spatiotemporal sequential logistics trajectory [44]. • Marcos et al. studied both the environment and approaches to conduct BDA, such as data management, model development, visualization, user interaction, and business models [45]. • Kim reported several successful cases of big data application. Examples include analysis of competing scenarios through 66,000 simulated elections conducted per day to understand the
- 26. decisions of individual voters during the 2012 reelection campaign of former US president Barack Obama and delivery routes and time management based on vehicle and parcel locations adopted by UPS, a US courier service company [46]. • Wang et al. redefined big data business analytics of logistics and supply chain management as supply chain analytics and discussed its importance [47]. • Queiroz and Telles studied the level of awareness of BDA in Brazilian companies through surveys conducted via questionnaires and proposed a framework to analyze companies’ maturity in implementing BDA projects in logistics and supply chain management [48]. • Hopkins analyzed the impact of BDA and Internet of things (IoT), such as truck telematics and geo-information in supporting large logistics companies to improve drivers’ safety and operating cost-efficiency [49]. The above examples of big data/BDA used by governments or corporations, as well as entities dealing with methods in either specific or general areas, make it clear that there is an abundance of studies on the need for and efficiency of big data. However, big data and BDA have not been studied until recently, and few studies use real corporate examples— especially in the logistics industry—that provide valuable business insights through detailed methods and results. Researchers should endeavor to provide second-hand experience through specific case studies
- 27. using big data/BDA-based BI, and then accumulate and integrate such case studies to establish a database of integrated convergent knowledge. This could enable corporations to adjust to changing environments and improve the productivity and efficiency of the organization. Sustainability 2018, 10, 3778 5 of 15 3. Practical Business Application The present study aims to examine the overall status of the logistics industry (an industry with continuously growing demand and prominence) and the courier service industry (an industry used by more consumers than any other logistics market segment) as well as business applications related to big data/BDA and BI. The final aim is to assist corporations in reducing trial-and-error periods in management, establishing long-term strategies, and enhancing cost-effectiveness of the corporations. 3.1. Courier Service Overview Given consumers’ increasing focus on personal service and convenience in consumer products, as well as global economic development, the manufacturing sector is converting from mass production of limited items to multi-item, small-scale production. This is rapidly increasing the volume and sales of courier services as more consumers buy online. Increased online purchases are also a result of ICT advances. According to the Korean Statistical Information Service, Korea’s e-retail sales amounted
- 28. to KRW 79,954,478 million in 2017, an increase of 21.85% from KRW 65,617,046 million in 2016, and a massive 107.69% increase from 2013 [50]. The courier service industry has become the biggest beneficiary of this dramatic increase in the volume of goods transported and is a suitable yardstick to measure the growth of the logistics industry [51,52]. Traditionally, logistics was considered a support industry for manufacturing and consumption and was mainly perceived as a cost, but it has since emerged as the main industry connecting producers and consumers. Manufacturing corporations regard supply expansion based on ICT to meet consumers’ demands as a key growth strategy, and the courier service industry has shown remarkable growth owing to the sharp increase in the need for parcel transportation [53]. A courier service is generally defined as comprising the entire process of transportation, from receiving a parcel to packaging, transporting, and delivering the parcel to the final destination under the transporter’s responsibility and at the customer’s request [54,55]. The courier service industry usually faces oligopolistic market competition, as it is an enormous service system that requires huge initial investment. Courier service companies are normally large operational organizations that deal with large amounts of cargo, hub terminals, general information systems, and a wide range of transportation vehicles and consist of a complicated network of labor and equipment [51]. Davis previously examined the usefulness of courier services by using information technology in the logistics industry [56]. DeLone and McLean showed that
- 29. a successful information system environment is a significant factor influencing user satisfaction as it models its influences on individuals and organizations [57]. Kim et al. focused on the use of transportation routes, freight distribution centers, and brokerage points for efficient parcel transportation via main roads [58]. Visser and Lanzendorf [59] analyzed the effects of business-to- consumer (B2C) e-commerce for cargo transportation, revealing that an increase in the demand for courier services leads to changes in freight per ton, distance, size, and fill rate of trucks. The authors illustrated the relationship between consolidation and transportation routes in courier companies [59]. Jeong et al. discussed the allocation of service centers to terminals with a given number of cargo terminals and locations [60], while Goh and Min examined the time of delivery by the capacity of cargo terminals [61]. Meanwhile, Sherif et al. presented an integrated model of the number and location of warehouses, allocation of customers to warehouses, and number and routes of vehicles to minimize transportation cost, fixed cost, operational cost, and route cost [62]. Lim et al. focused on the improvement of service quality while considering price reduction due to the increase of online demand, volume of delivery, and short-term responses, as well as the lack of mid- and long-term responses due to increase in online transactions [63]. Park et al. investigated methods of increasing productivity while considering both logistics and employees by utilizing a wireless Internet system [64], while Kim and Choi explored the effects of a corporation’s logistics technology on courier services based on online shopping malls as courier service users [65].
- 30. In summary, most previous research concerning the courier service industry focused on the analysis of courier service networks and delivery efficiency in terms of optimal logistics structures, Sustainability 2018, 10, 3778 6 of 15 methods for improving service quality, and minimization of costs in terms of operational requirements. Only a few case studies gathered and analyzed big data or BI applications in the field, considering the increase in e-commerce delivery demand. 3.2. Case Study: CJ Logistics This study uses the case of CJ Logistics, Korea’s largest logistics company. It examines the sorting process, especially regarding decisions about loading/unloading docks and hub terminals, which are at the core of courier services, to examine the effective use of big data/BDA through BI. CJ Logistics was selected as the research subject as it is the largest logistics service provider in Korea with the highest market share and sales revenue of KRW 7110.3 billion in 2017 [66]. In addition, as shown in Figure 1 (big data case of CJ Logistics, March 2018), the company is an innovation leader in the industry. It is traditionally considered a 3D business that uses BI based on high-tech automation-oriented technology, engineering, and system and solution plus consulting (TES + C), while actively and rapidly adopting big data/BDA at the same time.
- 31. Sustainability 2018, 10, x FOR PEER REVIEW 6 of 15 3.2. Case Study: CJ Logistics This study uses the case of CJ Logistics, Korea’s largest logistics company. It examines the sorting process, especially regarding decisions about loading/unloading docks and hub terminals, which are at the core of courier services, to examine the effective use of big data/BDA through BI. CJ Logistics was selected as the research subject as it is the largest logistics service provider in Korea with the highest market share and sales revenue of KRW 7110.3 billion in 2017 [66]. In addition, as shown in Figure 1 (big data case of CJ Logistics, March 2018), the company is an innovation leader in the industry. It is traditionally considered a 3D business that uses BI based on high-tech automation-oriented technology, engineering, and system and solution plus consulting (TES + C), while actively and rapidly adopting big data/BDA at the same time. Figure 1. Technology, engineering, system and solution plus consulting (TES + C) of CJ Logistics. CJ Logistics is a market leader equipped with cutting-edge logistics technologies, including real- time tracking of freight, an integrated courier and freight tracking system that enables users to view customer information and requirements, satellite vehicle tracking, and temperature control systems
- 32. [67]. In 2017, CJ Logistics invested more than KRW 120 billion to automate its sorting process through sub-terminals to aid sustainable growth. CJ Logistics’ infrastructure is more than three times bigger than that of its closest competitor in the courier service industry. With five hub terminals, more than 270 sub-terminals, and more than 16,000 vehicles, CJ Logistics processes more than 5.3 million packages per day. Its mega hub terminal in Gwangju, Gyeonggi- do Province—which was due for completion in August 2018 with an investment of more than KRW 400 billion—will utilize convergence technologies such as big data, robots, and IoT to expand its services for the convenience of its customers across Korea. This will include same-day delivery, same-day return, and scheduled delivery services. The company is simultaneously moving forward with its planned international growth. At the end of 2017, CJ Logistics had a global network of 238 centers in 137 cities and 32 countries. It opened the Shenyang Flagship Center, a mammoth logistics center in Shenyang, China, on 15 June 2018. The purpose of this investment was to accelerate the company’s business in northern Asia, including three provinces of northeastern China— Liaoning, Jilin, and Heilongjiang. The company has implemented huge capital expenditure to broaden its business efficiently, laying the groundwork for sustainable growth and expansion by raising the entrance barrier for rivals (big data case of CJ Logistics, March 2018). CJ Logistics mainly uses a hub-and-spoke system, which connects points via hubs or logistics centers dealing with massive cargo volumes in its courier service; it also uses a point-to-point
- 33. operational system directly connecting origins and destinations. The point-to-point system delivers Figure 1. Technology, engineering, system and solution plus consulting (TES + C) of CJ Logistics. CJ Logistics is a market leader equipped with cutting-edge logistics technologies, including real-time tracking of freight, an integrated courier and freight tracking system that enables users to view customer information and requirements, satellite vehicle tracking, and temperature control systems [67]. In 2017, CJ Logistics invested more than KRW 120 billion to automate its sorting process through sub-terminals to aid sustainable growth. CJ Logistics’ infrastructure is more than three times bigger than that of its closest competitor in the courier service industry. With five hub terminals, more than 270 sub-terminals, and more than 16,000 vehicles, CJ Logistics processes more than 5.3 million packages per day. Its mega hub terminal in Gwangju, Gyeonggi-do Province—which was due for completion in August 2018 with an investment of more than KRW 400 billion—will utilize convergence technologies such as big data, robots, and IoT to expand its services for the convenience of its customers across Korea. This will include same-day delivery, same-day return, and scheduled delivery services. The company is simultaneously moving forward with its planned international growth. At the end of 2017, CJ Logistics had a global network of 238 centers in 137 cities and 32 countries. It opened the Shenyang Flagship Center, a mammoth logistics center in Shenyang, China, on 15 June 2018. The purpose of this investment was to accelerate the company’s business in
- 34. northern Asia, including three provinces of northeastern China—Liaoning, Jilin, and Heilongjiang. The company has implemented huge capital expenditure to broaden its business efficiently, laying the Sustainability 2018, 10, 3778 7 of 15 groundwork for sustainable growth and expansion by raising the entrance barrier for rivals (big data case of CJ Logistics, March 2018). CJ Logistics mainly uses a hub-and-spoke system, which connects points via hubs or logistics centers dealing with massive cargo volumes in its courier service; it also uses a point-to-point operational system directly connecting origins and destinations. The point-to-point system delivers to and from terminals, saving time on package arrivals while alleviating capacity issues during the peak season. However, growing volumes may increase costs, as they require more investment in terminals; a volume imbalance among terminals can cause unnecessary additional costs. On the other hand, in the hub-and-spoke system, packages are gathered and sorted in a large terminal before being delivered to a destination terminal. The advantage of this system is that it reduces arrival time to the terminals, easing the imbalance in volume. However, the disadvantages are that it may delay deliveries to distant or rural areas during the peak season and requires a large-scale hub terminal [67]. Since CJ Logistics mostly uses the hub-and-spoke system, whose core is the logistics process
- 35. at the hub terminal, this study focuses on decisions concerning the loading/unloading docks in the process. This focus point was selected for the following reasons. First, few previous studies have focused on this segment, even though it has greater room for improvement regarding productivity and efficiency than other segments. Second, the importance of this segment may have been overlooked, since standardizing the process is challenging owing to differences in the environment, such as the distance between buildings or shape of the space. Third, there are many other difficulties to address, including outsourcing, warehouse management, freight payment, inventory management, packing, customs clearance, and customer claims [51]. Many courier service providers allocate hub terminal docks for loading/unloading simply according to terminal conditions, such as the distance between docks and number of packages, mostly based on past experience. By contrast, CJ Logistics has dramatically improved productivity and efficiency by “seeing the unseen” through the use of big data/BDA and promoting faster and better decision making through BI. The hub terminal process was selected from the three general stages of courier services, namely, pick-up, transport/sorting, and delivery (Figure 2). This process was selected because it is the central process connecting pick-ups from different locations with delivery to different destinations [68,69]. Sustainability 2018, 10, x FOR PEER REVIEW 7 of 15 to and from terminals, saving time on package arrivals while
- 36. alleviating capacity issues during the peak season. However, growing volumes may increase costs, as they require more investment in terminals; a volume imbalance among terminals can cause unnecessary additional costs. On the other hand, in the hub-and-spoke system, packages are gathered and sorted in a large terminal before being delivered to a destination terminal. The advantage of this system is that it reduces arrival time to the terminals, easing the imbalance in volume. However, the disadvantages are that it may delay deliveries to distant or rural areas during the peak season and requires a large-scale hub terminal [67]. Since CJ Logistics mostly uses the hub-and-spoke system, whose core is the logistics process at the hub terminal, this study focuses on decisions concerning the loading/unloading docks in the process. This focus point was selected for the following reasons. First, few previous studies have focused on this segment, even though it has greater room for improvement regarding productivity and efficiency than other segments. Second, the importance of this segment may have been overlooked, since standardizing the process is challenging owing to differences in the environment, such as the distance between buildings or shape of the space. Third, there are many other difficulties to address, including outsourcing, warehouse management, freight payment, inventory management, packing, customs clearance, and customer claims [51]. Many courier service providers allocate hub terminal docks for loading/unloading simply according to terminal conditions, such as the distance between docks and number of packages, mostly
- 37. based on past experience. By contrast, CJ Logistics has dramatically improved productivity and efficiency by “seeing the unseen” through the use of big data/BDA and promoting faster and better decision making through BI. The hub terminal process was selected from the three general stages of courier services, namely, pick-up, transport/sorting, and delivery (Figure 2). This process was selected because it is the central process connecting pick-ups from different locations with delivery to different destinations [68,69]. Figure 2. General courier service structure. An incident that occurs at the hub terminal can have a serious impact on the entire cycle—from pick-up to delivery—and could cause a bottleneck effect at hub terminals. This is a significant issue that needs to be addressed to secure growth in the industry, as it can paralyze transportation and delivery within a company on a large scale. Resolving this issue alongside difficulties in other areas by using big data/BDA could improve company productivity and efficiency as a whole. Figure 2. General courier service structure. Sustainability 2018, 10, 3778 8 of 15 An incident that occurs at the hub terminal can have a serious impact on the entire cycle—from pick-up to delivery—and could cause a bottleneck effect at hub
- 38. terminals. This is a significant issue that needs to be addressed to secure growth in the industry, as it can paralyze transportation and delivery within a company on a large scale. Resolving this issue alongside difficulties in other areas by using big data/BDA could improve company productivity and efficiency as a whole. 3.2.1. Data and Methodology CJ Logistics witnessed a drastic rise in online and offline B2C transactions, experiencing a compound annual growth rate of 9.9% from 2011 to 2016. In addition, the courier company’s market share rose from 42% in 2015 to 46% in 2017. To accommodate this growth, the company increased the number and size of its vehicles, established a demand forecasting system, and improved its peer-to-peer (P2P) network. These measures increased the daily delivery per person from 262 boxes to 344 boxes between 2015 and 2017, while the sorting capacity of hub terminals was improved from around 4.4 million cases to 5.3 million cases during the same period. However, since the company’s hub terminal capacity had reached its limit, bottlenecks in the logistics process were becoming serious. As a result, the rate of remaining cargo increased by 3.1%, and the overnight delivery rate dropped by 2.3% between 2015 and 2017. This situation makes it clear that it is imperative for the company to find a solution through methods that could enhance hub terminal capacity. To address this issue, CJ Logistics decided to integrate BDA into its existing decision-making processes to understand the current situation better, enabling the
- 39. company to make better-informed choices and identify future directions. Daejeon hub was chosen for the pilot test. First, information was gathered on roughly 75 million inbound invoices and 240 million packages at Daejeon hub terminal out of a total of 260 million inbound invoices and 720 million packages at hub terminals. The information was gathered over a three-month period between November 2016 and January 2017. This information was used to generate extensive data on the unloading docks at the hub terminal as well as on routes, transition points, moving time, loading docks, remaining cargo, and sorting personnel for BDA. Based on the results, the shortest distance between loading and unloading docks, time metrics, and vehicle loading information were integrated with application methods (as shown later in this subsection). The simulation produced results that would have been impossible to obtain by conventional dock allocation methods that are based on classification codes and number of packages. By reflecting the results at different sites, CJ Logistics was able to increase its hub terminal capacity, as shown in the following paragraphs. Packages delivered by customers are collected at sub-terminals in each region and transported to hub terminals by truck. Vehicles entering the hub terminal wait for dock allocation and are then unloaded or loaded after being allocated, as per the process shown in Figure 3. In the entire dock allocation process, CJ Logistics reflected at least two types of objective functions to identify the first-in-line vehicle to unload among those waiting, the closest unloading chute, and the second-in-line chute and vehicle in terms of waiting time while unloading
- 40. vehicles to optimize dock allocation in the hub terminal. Objective function (1) sets the weighting factor for unloading priority and reflects the number of packages using the volume information in the vehicles for application based on four types of “reference information”, namely, (1) loading priority of waiting vehicles by route; (2) customer classification according to special sale customers, premium customers, and general customers; (3) vehicle classification according to unloading only, unloading/loading, and loading only; and (4) content classification according to console, produce, and general. These unloading priorities were set within the “constraints” of the remaining vehicles that had not been unloaded, and vehicles waiting for more than three hours that should have been unloaded first. Table 1 presents vehicle unloading priorities based on weighting factor and time. Sustainability 2018, 10, 3778 9 of 15 Sustainability 2018, 10, x FOR PEER REVIEW 9 of 15 Figure 3. Optimization of dock allocation process. Objective function (1): Selection of vehicles to unload first = ∗ (1) W: weighting factor for unloading priority, N: number of packages.
- 41. Table 1. Selection of vehicle unloading priorities according to weighting factor and time. Order Category W (Before 0:00) W (After 0:00) 1 Special sale customer 50 3 2 Route for loading first 30 50 3 Console volume 8 15 4 Produce 7 10 5 Premium customer 3 20 6 First-in, first-out (FIFO) 2 2 Note: W: weighting factor for unloading priority. Objective function (2) pertains to optimum unloading chute allocation. This was calculated using volume by loading chute for each vehicle, travel time between unloading/loading chutes, content information, and reflected travel time under the constraints. The function includes minimization of congestion through equal allocation of vehicles, minimization of travel between buildings, and allocation of vehicles with more than 30% console content to a special console unloading zone, based on two types of reference information. The reference information includes (1) travel time between Figure 3. Optimization of dock allocation process. Objective function (1): Selection of vehicles to unload first Selection of vehicles to unload first = ∑( W ∗ N ) (1) W: weighting factor for unloading priority, N: number of packages. Table 1. Selection of vehicle unloading priorities according to
- 42. weighting factor and time. Order Category W (Before 0:00) W (After 0:00) 1 Special sale customer 50 3 2 Route for loading first 30 50 3 Console volume 8 15 4 Produce 7 10 5 Premium customer 3 20 6 First-in, first-out (FIFO) 2 2 Note: W: weighting factor for unloading priority. Objective function (2) pertains to optimum unloading chute allocation. This was calculated using volume by loading chute for each vehicle, travel time between unloading/loading chutes, content information, and reflected travel time under the constraints. The function includes minimization of congestion through equal allocation of vehicles, minimization of travel between buildings, and allocation of vehicles with more than 30% console content to a special console unloading zone, based on two types of reference information. The reference information includes (1) travel time between loading/unloading chutes and (2) unloading service time for maximum, minimum, and average volume. Sustainability 2018, 10, 3778 10 of 15 Objective function (2): Optimum unloading chute allocation Optimum unloading chute allocation = ∑( L ∗ T ) (2)
- 43. L: Volume in the vehicles by loading chutes, T: Travel time between loading/unloading chutes. Although vehicles are assigned to docks through optimum chutes, by considering operational status at the docks and the fact that unloading procedures can change at any time, the function repeats the optimization of the dock allocation process to decide whether a vehicle should be placed on hold or assigned to a second dock, or whether a second-in-line vehicle should be sent first to increase efficiency. Information from the BDA was used in connection with balancing the volume among loading docks through tracking analysis of individual products, fast delivery by development of new P2P routes, expansion of hub terminal capacity, and volume analysis of products for higher productivity and efficiency. 3.2.2. Simulation and Adoption Result On 6 November 2016, vehicle number “98 Ba 3490” loaded with cargo from Jungrang sub-terminal arrived at Daejeon hub terminal, unloaded, and then should have reloaded 249 items (52.8% of the total load) on the B1 and 1st floors of Building A, 177 items (37.5% of the total load) on the 1st and 2nd floors of Building B, and 46 items (9.7% of the total load) on the 1st floor of Building C as can be seen in Figure 4a, and the number of items in the red box indicate the quantity that should be loaded in the individual dock. Therefore, the vehicle was allocated to Dock D7 of Building A, since there were more packages to load at Building A than at the other docks (see the purple dot in Figure 4a). It took 57 min
- 44. and 34 s to complete the unloading/loading process. However, a simulation based on big data/BDA revealed that dock allocation according to the number of items to load, as shown earlier in this subsection, was very inefficient. The choice of Dock D7, Building A was ranked 41st, as evident from the ranking table in Figure 4b, in terms of efficiency, and unloading at Dock F8, Building B proved most efficient (see the blue dot in Figure 4b). This information could not be determined before the BDA. The simulation results showed that unloading at Dock F8, Building B could decrease the vehicle’s travel time to around one-fifth of the actual time it took when using Dock D7, Building A. The actual travel time was three times greater than the simulated travel time. When a simulation was conducted using the entire fleet of vehicles, the overall efficiency of the hub terminal rose, reducing travel time by more than 20 min, even when unloading at Dock D7, Building A. CJ Logistics shared the simulation results through the internal reporting system using BI, thus enabling management to make decisions optimizing dock allocations and considering the flow of cargo traffic in hub terminals. As a result, the flow of products improved dramatically, raising the processing rate per hour as well as the rate of overnight deliveries, while lowering the rate of remaining freight. In Daejeon hub terminal, the average distribution time per vehicle was 52 min and 42 s during the thanksgiving season in 2016. This time decreased to 44 min and 7 s during the same period in 2017, a remarkable improvement of 16.3%. Building on such positive results, CJ Logistics subdivided the
- 45. distribution model by days of the week, seasons, and events, and fine-tuned the metrics of optimum paths. This system was applied to mega hubs in metropolitan areas. By late 2017, the system had been applied throughout the country. The remaining cargo was reduced by 14% from the previous year, and the overnight delivery rate increased by 2.8% in 2017. In summary, CJ Logistics achieved a phenomenal rise in productivity and cost-effectiveness through the use of big data/BDA. It still used the existing infrastructure but expanded the application of BI based on BDA to make decisions across business segments, for long-term strategies, and for additional investment by management. Sustainability 2018, 10, 3778 11 of 15 Sustainability 2018, 10, x FOR PEER REVIEW 11 of 15 Figure 4. (a) Before optimization of dock allocation; DaeJeon Hub Terminal of CJ Logistics; (b) After optimization of dock allocation using BDA; DaeJeon Hub Terminal of CJ Logistics. 4. Discussion and Conclusions Business activities that are believed to be sufficiently empirical and productive to ensure efficiency can benefit from different perspectives and breakthroughs upon acquiring and analyzing big data, and can be realized through BI. The value of big data depends on the types of data extracted and how they are utilized. The crucial factor, however, is the
- 46. method of turning raw data into valuable information, and not the quality or quantity of the data. Therefore, it is vital to identify the type and scope of data to be collected according to their purpose and focus area. The efficient use of Figure 4. (a) Before optimization of dock allocation; DaeJeon Hub Terminal of CJ Logistics; (b) After optimization of dock allocation using BDA; DaeJeon Hub Terminal of CJ Logistics. 4. Discussion and Conclusions Business activities that are believed to be sufficiently empirical and productive to ensure efficiency can benefit from different perspectives and breakthroughs upon acquiring and analyzing big data, and can be realized through BI. The value of big data depends on the types of data extracted and how they are utilized. The crucial factor, however, is the method of turning raw data into valuable information, and not the quality or quantity of the data. Therefore, it is vital to identify the type and scope of data to be collected according to their purpose and focus area. The efficient use of big data Sustainability 2018, 10, 3778 12 of 15 may provide an opportunity to a small or medium enterprise to become a large corporation or market leader by taking advantage of meaningful information, and for a large corporation to maintain its market share and ensure sustainable growth and competitiveness. Many studies have been conducted
- 47. on BI, big data, and BDA so far, but for enterprises to implement changes, it is necessary for them to understand intuitively that BI, big data, and BDA cannot be separated, but should be integrated and utilized in the management decision support system as a whole. As the case study of CJ Logistics shows, the process of collecting and analyzing big data and applying it through BI is separated neither individually nor progressively. The limitations of this case study include the facts that the big data have been derived from a limited date range, there are differences in the infrastructure and situation of each company, and the case study represents only a portion of a company within a specific industry. Nonetheless, we believe that this case study can be directly applied to other logistics companies within the same sector and, therefore, can help these companies achieve time and cost efficiency without much trial and error. Our study can also have a positive long-run impact by informing companies in the logistics industry, as well as in other industries, of the possibility of increasing the efficiency and productivity of their existing infrastructure without additional investment. CJ Logistics’ process of expanding and applying the experience gained through the combined use of BI, big data, and BDA to all of its business divisions can be a valuable example for other companies and may provide insights concerning future business directions and reduced trial and error. Future studies can expand on this research to provide practical knowledge and experience by collecting and sharing similar case studies, including those about volumetric analysis through ITS (Intelligence Scanner) of goods, volume management through
- 48. production of boxes for each customer, classification of customers based on volume density, and etc. which are based on practical business applications to build integrated knowledge. Author Contributions: Conceptualization, D.-H.J. and H.-J.K.; methodology, D.-H.J.; software, D.-H.J.; validation, D.-H.J. and H.-J.K.; formal analysis, D.-H.J.; investigation, D.- H.J.; resources, D.-H.J.; data curation, D.-H.J.; writing—original draft preparation, D.-H.J.; writing—review and editing, D.-H.J. and H.-J.K.; visualization, D.-H.J.; supervision, D.-H.J. and H.-J.K.; project administration, D.-H.J. and H.-J.K. Funding: This research received no external funding. Conflicts of Interest: CJ Logistics provided some part of the data for the case study to Dong Hui Jin and validated all the data used in this study. References 1. Laney, D. 3D Data Management: Controlling Data Volume, Velocity and Variety; Application Delivery Strategy; META Group: Stamford, CT, USA, 2001; Volume 949. 2. McAfee, A.; Brynjolfsson, E. Big data: The management revolution. Harv. Bus. Rev. 2012, 90, 60–68. [PubMed] 3. Fosso Wamba, S.; Akter, S.; Edwards, A.; Chopin, G.; Gnanzou, D. How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 2015, 165, 234–246. [CrossRef] 4. Wang, Y.; Kung, L.; Wang, W.Y.C.; Cegielski, C.G. An
- 49. integrated big data analytics-enabled transformation model: Application to health care. Inf. Manag. 2018, 55, 64–79. [CrossRef] 5. White, M. Digital workplaces: Vision and reality. Bus. Inf. Rev. 2012, 209, 205–214. [CrossRef] 6. Kambatla, K.; Kollias, G.; Kumar, V.; Grama, A. Trends in big data analytics. J. Parallel Distrib. Comput. 2014, 74, 2561–2573. [CrossRef] 7. Addo-Tenkorang, R.; Helo, P.T. Big data applications in operations/supply-chain management: A literature review. Comput. Ind. Eng. 2016, 101, 528–543. [CrossRef] 8. Richey, R.G.; Morgan, T.R.; Lindsey-Hall, K.; Adams, F.G. A global exploration of big data in the supply chain. Int. J. Phys. Distrib. Logist. Manag. 2016, 46, 710–739. [CrossRef] 9. Yu, W.; Chavez, R.; Jacobs, M.A.; Feng, M. Data-driven supply chain capabilities and performance: A resource-based view. Transp. Res. E Logist. 2018, 114, 371– 385. [CrossRef] http://www.ncbi.nlm.nih.gov/pubmed/23074865 http://dx.doi.org/10.1016/j.ijpe.2014.12.031 http://dx.doi.org/10.1016/j.im.2017.04.001 http://dx.doi.org/10.1177/0266382112470412 http://dx.doi.org/10.1016/j.jpdc.2014.01.003 http://dx.doi.org/10.1016/j.cie.2016.09.023 http://dx.doi.org/10.1108/IJPDLM-05-2016-0134 http://dx.doi.org/10.1016/j.tre.2017.04.002 Sustainability 2018, 10, 3778 13 of 15
- 50. 10. Roßmann, B.; Canzaniello, A.; Von der Gracht, H.; Hartmann, E. The future and social impact of Big Data Analytics in Supply Chain Management: Results from a Delphi study. Technol. Forecast. Soc. Chang. 2018, 130, 135–149. [CrossRef] 11. Russom, P. Big Data Analytics. TDWI Best Practices Report, Fourth Quarter. 2011. Available online: tdwi.org (accessed on 12 July 2018). 12. Rouse, M. Big Data Analytics. 2012. Available online: http://searchbusinessanalytics.techtarget.com/ definition/big-data-analytics (accessed on 12 July 2018). 13. LaValle, S.; Lesser, E.; Shockley, R.; Hopkins, M.S.; Kruschwitz, N. Big data, analytics and the path from insights to value. MIT Sloan Manag. Rev. 2013, 52, 21–31. 14. Loshin, D. Big Data Analytics: From Strategic Planning to Enterprise Integration with Tools, Techniques, NoSQL, and Graph; Elsevier: Waltham, MA, USA, 2013. 15. Tiwari, S.; Wee, H.M.; Daryanto, Y. Big data analytics in supply chain management between 2010 and 2016: Insights to industries. Comput. Ind. Eng. 2018, 115, 319–330. [CrossRef] 16. Gilad, B.; Herring, J.P. The Art and Science of Business Intelligence Analysis; JAI Press Ltd.: Greenwich, UK, 1996. 17. Davenport, T.; Prusak, L. Working Knowledge; HBS Press: Boston, MA, USA, 1998. 18. Berson, A.; Smith, S.; Thearling, K. Building Data Mining Application for CRM; McGraw-Hill: New York, NY, USA, 2000.
- 51. 19. Simon, A.; Shaffer, S. Data Warehousing and Business Intelligence for E-Commerce; Morgan Kaufmann Publishers: San Francisco, CA, USA, 2001. 20. Solomon, N. Business intelligence. Commun. Assoc. Inf. Syst. 2004, 13, 177–195. 21. Fan, S.; Raymond, Y.K.; Lau, J.; Zhaob, L. Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Res. 2015, 2, 28–32. [CrossRef] 22. Manyika, J.; Chui, M.; Brown, B.; Bughin, J.; Dobbs, R.; Roxburgh, C.; Byers, A.H. Big Data: The Next Frontier for Innovation, Competition, and Productivity; McKinsey Global Institute: Washington, DC, USA, 2011. 23. Kang, M.; Kim, S.; Park, S. Analysis and utilization of big data. J. Inf. Sci. Soc. 2012, 30, 25–32. 24. Liang, T.; Liu, Y. Research landscape of business intelligence and big data analytics: A bibliometrics study. Expert Syst. Appl. 2018, 111, 2–10. [CrossRef] 25. Tankard, C. Big data security. Netw. Secur. 2012, 7, 5–8. [CrossRef] 26. Ram, J.; Zhang, C.; Koronios, A. The implications of big data analytics on business intelligence: A qualitative study in China. Procedia Comput. Sci. 2016, 87, 221–226. [CrossRef] 27. Wang, L.; Alexander, C.A. Big data driven supply chain management and business administration. Am. J. Econ. Bus. Adm. 2015, 7, 60–67. [CrossRef] 28. Vera-Baquero, A.; Palacios, R.C.; Stantchev, V.; Molloy, O. Leveraging big-data for business process analytics.
- 52. Learn. Organ. 2015, 22, 215–228. [CrossRef] 29. Tan, K.H.; Zhan, Y.Z.; Ji, G.; Ye, F.; Chang, C. Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph. Int. J. Prod. Econ. 2015, 165, 223–233. [CrossRef] 30. Miškuf, M.; Zolotová, I. Application of Business Intelligence Solution s on Manufacturing Data. In Proceedings of the 13th International Symposium on Applied Machine Intelligence and Informatics, Herl’any, Slovakia, 22–24 January 2015. 31. Luhn, H.P. A business intelligence system. IBM J. Res. Dev. 1958, 2, 314–319. [CrossRef] 32. Vitt, E.; Luckevich, M.; Misner, S. Business Intelligence; Microsoft Press: Redmond, WA, USA, 2002. 33. Turban, E.; Aronson, J.E.; Liang, T.P. Decision Support and Intelligence Systems; Prentice-Hall: Upper Saddle River, NJ, USA, 2005. 34. Van-Hau, T. Getting value from business intelligence
- 53. systems: A review and research agenda. Decis. Support Syst. 2017, 93, 111–124. 35. Turban, E.; Volonino, L. Information Technology for Management, 7th ed.; John Wiley & Sons, Inc.: New York, NY, USA, 2010. 36. Larsona, D.; Chang, V. A review and future direction of agile, business intelligence, analytics and data science. Int. J. Inf. Manag. 2016, 36, 700–710. [CrossRef] 37. Chen, H.; Chiang, R.H.; Storey, V.C. Business intelligence and analytics: From big data to big impact. MIS Q. 2012, 36, 1165–1188. [CrossRef] http://dx.doi.org/10.1016/j.techfore.2017.10.005 tdwi.org http://searchbusinessanalytics.techtarget.com/definition/big- data-analytics http://searchbusinessanalytics.techtarget.com/definition/big- data-analytics http://dx.doi.org/10.1016/j.cie.2017.11.017 http://dx.doi.org/10.1016/j.bdr.2015.02.006 http://dx.doi.org/10.1016/j.eswa.2018.05.018
- 54. http://dx.doi.org/10.1016/S1353-4858(12)70063-6 http://dx.doi.org/10.1016/j.procs.2016.05.152 http://dx.doi.org/10.3844/ajebasp.2015.60.67 http://dx.doi.org/10.1108/TLO-05-2014-0023 http://dx.doi.org/10.1016/j.ijpe.2014.12.034 http://dx.doi.org/10.1147/rd.24.0314 http://dx.doi.org/10.1016/j.ijinfomgt.2016.04.013 http://dx.doi.org/10.2307/41703503 Sustainability 2018, 10, 3778 14 of 15 38. Wixom, B.; Ariyachandra, T.; Douglas, D.; Goul, M.; Gupta, B.; Iyer, L.; Kulkarni, U.; Mooney, J.G.; Phillips-Wren, G.; Turetken, O. The current state of business intelligence in academia: The arrival of big data. Commun. Assoc. Inf. Syst. 2014, 34, 1–13. 39. Bala, M.; Balachandran, S.P. Challenges and benefits of deploying big data analytics in the cloud for business intelligence. Procedia Comput. Sci. 2017, 112, 1112–1122. 40. Davenport, T.H. How strategists use ‘big data’ to support internal business decisions, discovery and production. Strat. Leadersh. 2014, 42, 45–50. [CrossRef]
- 55. 41. Narayanan, V. Using big-data analytics to manage data deluge and unlock real-time business insights. J. Equip. Lease Financ. 2014, 32, 1–7. 42. Erevelles, S.; Fukawa, N.; Swayne, L. Big data consumer analytics and the transformation of marketing. J. Bus. Res. 2016, 69, 897–904. [CrossRef] 43. Lazer, D.; Kennedy, R.; King, G.; Vespignani, A. The parable of Google flu: Traps in big data analysis. Science 2014, 343, 1203–1205. [CrossRef] [PubMed] 44. Zhong, R.Y.; Huang, G.Q.; Lan, S.; Dai, Q.Y.; Chen, X.; Zhang, T. A big data approach for logistics trajectory discovery from RFID-enabled production data. Int. J. Prod. Econ. 2015, 165, 260–272. [CrossRef] 45. Marcos, D.; Assunção, R.N.; Calheiros, S.B.; Marco, A.S.; Netto, R.B. Big data computing and clouds: Trends and future directions. J. Parallel Distrib. Comput. 2015, 79, 3–15. 46. Kim, Y. Enterprise innovation through the introduction of big data-based advanced analysis system: Case and
- 56. methodology. IE Mag. 2013, 20, 43–49. 47. Wang, G.; Gunasekaran, A.; Ngai, E.W.T.; Papadopoulos, T. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. Int. J. Prod. Econ. 2016, 176, 98–110. [CrossRef] 48. Queiroz, M.M.; Telles, R. Big data analytics in supply chain and logistics: An empirical approach. Int. J. Logist. Manag. 2018, 29, 767–783. [CrossRef] 49. Hopkins, J.; Hawking, P. Big data analytics and IoT in logistics: A case study. Int. J. Logist. Manag. 2018, 29, 575–591. [CrossRef] 50. Korean Statistical Information Service. 2018. Available online: http://kostat.go.kr/portal/korea/index. action (accessed on 18 October 2018). 51. Jo, Y.; Yoon, M. Analysis of courier service market of South Korea. Korea Technol. Innov. Soc. 2001, 245–270. 52. Ho, J.S.Y.; Teik, D.O.L.; Tiffany, F.; Kok, L.F.; The, T.Y. Logistic Service Quality among Courier Services in
- 57. Malaysia. In International Conference on Economics, Business Innovation, IPEDR; IACSIT Press: Singapore, 2012; Volume 38, pp. 113–117. 53. Lee, C. A study on the strengthening competitiveness of railway logistics business with the growth of the courier business. J. Vocat. Rehabil. 2002, 25, 75–95. 54. Park, Y. A study on the domestic express courier service’s present situation and further theme in the era of e-commerce. Korean J. Bus. Adm. 2003, 39, 1425–1444. 55. Jung, J.; Kim, C. A Study on Quality Improvement of Courier Service Using Quality Function Deployment. In Proceedings of the Conference of the Korean Society of Business Administration, Seoul, Korea, 24–25 November 2015; pp. 451–468. 56. Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 361–391. [CrossRef] 57. DeLone, W.H.; McLean, E.R. Information system success: The quest for the dependent variable. Inf. Syst. Res. 1992, 3, 60–92. [CrossRef]
- 58. 58. Kim, W.; Lim, S.; Park, S. Transportation plan of trunk transport problem with small quantity. Ind. Eng. 2000, 13, 471–478. 59. Visser, E.J.; Lanzendorf, M. Mobility and accessibility effects of b2c E-commerce: A literature survey. J. Soc. Econ. Geogr. 2004, 95, 189–205. 60. Jeong, K.; Goh, C.; Shin, J. Model for the assignment and scheduling of container transport vehicles. Logist. Res. 2005, 13, 141–154. 61. Goh, C.; Min, H. Cargo terminal capacity and order deadline time decision in courier service. Logist. Res. 2006, 14, 43–58. 62. Sherif, H.L.; Fattouh, M.; Issa, A. Location/allocation and routing decisions in supply chain network design. J. Model. Manag. 2006, 1, 173–183. http://dx.doi.org/10.1108/SL-05-2014-0034 http://dx.doi.org/10.1016/j.jbusres.2015.07.001 http://dx.doi.org/10.1126/science.1248506 http://www.ncbi.nlm.nih.gov/pubmed/24626916
- 59. http://dx.doi.org/10.1016/j.ijpe.2015.02.014 http://dx.doi.org/10.1016/j.ijpe.2016.03.014 http://dx.doi.org/10.1108/IJLM-05-2017-0116 http://dx.doi.org/10.1108/IJLM-05-2017-0109 http://kostat.go.kr/portal/korea/index.action http://kostat.go.kr/portal/korea/index.action http://dx.doi.org/10.2307/249008 http://dx.doi.org/10.1287/isre.3.1.60 Sustainability 2018, 10, 3778 15 of 15 63. Lim, H.; Lim, J.; Lee, H. An exploratory study on the effective operation of the logistics network for courier service by the growth of online shopping. Korea Mark. J. 2007, 9, 97–129. 64. Park, S.; Kang, Y.; Suh, Y. A study on the success factors of using wireless Internet system in logistics/courier service companies. Inf. Syst. Res. 2009, 18, 127–150. 65. Kim, S.; Choi, Y. Impact of logistics information technology on the satisfaction of courier service. J. Korea Port Econ. Assoc. 2011, 27, 91–112.
- 60. 66. Data Analysis, Retrieval, and Transfer System (DART) of Financial Supervisory Service; Understanding of CJ and Logistics Industry; CJ Group: Seoul, Korea, 2018. 67. Lee, S.; Jeong, I. A case study on comparative analysis of courier service information system. Bus. Intell. Res. 2009, 28, 1–24. 68. Korea Consumer Agency. Use of Courier Service and Survey; Korea Consumer Agency: Seoul, Korea, 2000. 69. Choi, K. System thinking for increasing the operational efficiency of courier service network. Korean Syst. Dyn. Res. 2011, 12, 89–114. © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). http://creativecommons.org/ http://creativecommons.org/licenses/by/4.0/.Introduction Literature Review Toward an Integrated Understanding of Big Data, BDA, and BI In-Depth Research through Case Studies
- 61. Practical Business Application Courier Service Overview Case Study: CJ Logistics Data and Methodology Simulation and Adoption Result Discussion and Conclusions References Malicious individuals have discovered several methods to attack and defeat cryptosystems. It's important that understand the threats posed by cryptographic attacks to minimize the risks to your network systems. Identify one cryptographic attack and how you can protect against it. Response Guidelines Participants must create a thread in order to view other threads in this forum. Main Post is due by the end of Wednesday (250 words). 2 Responses (100 words) using at least one of the following: · Ask a probing question. · Offer a suggestion. · Elaborate on a particular point. · Provide an alternative opinion. Review and summarize the attached journal article within 4 pages (does not include Title page, abstract and reference page). Do not use outside sources "only this article". You may use the article review template to guide you, however, your
- 62. submission must be in full APA format. · Title page with the Running head: · Abstract · Body (4 pages) . Conclusion · Reference (1) Reading and summarizing a research article: Authors’ last names (year) conducted a study about ________________________. The participants were/the setting was ___________________________. (New paragraph) The findings were _____________________________. Discussion. (Possibly a new paragraph) The authors suggested _____________________. Discussion. Students should fill in the blanks with their own words. To copy directly from the article fails to show comprehension and considered plagiarism. To “fill in the blanks”, a student should read the journal article and pay specific attention to: Sentence #1- Authors’ last names (year) conducted a study about _________________. · Read the Abstract; this will give an overview of the study’s
- 63. (article’s) purpose. · Read the entire article without trying to summarize it. · Go back and read the Literature Review or Background section of the article. Toward the end of the section, the authors should identify gaps in the existing literature and tell the reader how the current study will fill that gap. The authors will also state their hypothesis (purpose) at the end of this section. · Section #2 - The participants were/the setting was ___________________________. · Read the Methods section of the paper. In this section, the authors will describe how the data was collected, who was included in the sample, and any instruments used. · A reader might want to consider sample size, demographic characteristics, or any interesting protocol. · It is not necessary to report every fact (i.e., 35% of the participants were male, 71%) Section #3 - The findings were _____________________________. · Read the Findings section of the article. · Some statistics may be confusing. Pay attention to key words such as “increased”, “decreased”, “improved”, and “reduced”. · “No change” may also be considered a significant finding.
- 64. · Next, read the Discussion section. The authors will present the findings in general terms. Section #4 - The authors suggested _____________________. · Read the Discussion section and look for comments that the authors made about the intervention or program such as “Did it work?” or “Should it be continued?”. · Look for the author’s critique of why the study did or did not produce results. Did anything unexpected influence the findings? · The author may suggest a future line of research or “next steps” to improve the body of knowledge. Additional Considerations: · A literature review is a summary of what research has been completed in a topic area; it should be summarized in your own words. · Read the entire article first and then go back and take notes. Jot down notes in your own words. This increases comprehension as well as decreases the likelihood of plagiarism. · The review is written in third person; no “I” or “you”. · Not every detail or fact needs to be reported. A reader will obtain a copy of the article if more information is needed. · Write the literature review in the past tense; the research has already been completed.
- 65. · The article cannot “do”, “find”, or “say” anything. The authors are the people who conducted the study. · The above format is a guideline. It may be necessary to change the verbs or to expand an idea. Abstract Voice over Internet Protocol (VoIP) is an advanced telecommunication technology which transfers the voice/video over high speed network that provides advantages of flexibility, reliability and cost efficient advanced telecommunication features. Still the issues related to security are averting many organizations to accept VoIP cloud environment due to security threats, holes or vulnerabilities. So, the novel secured framework is absolutely necessary to prevent all kind of VoIP security issues. This paper points out the existing VoIP cloud architecture and various security attacks and issues in the existing framework. It also presents the defense mechanisms to prevent the attacks and proposes a new security framework called Intrusion Prevention System (IPS) using video watermarking and extraction technique and Liveness
- 66. Voice Detection (LVD) technique with biometric features such as face and voice. IPSs updated with new LVD features protect the VoIP services not only from attacks but also from misuses. A Comprehensive Survey of Security Issues and Defense Framework for VoIP Cloud Ashutosh Satapathy* and L. M. Jenila Livingston School of Computing Science and Engineering, VIT University, Chennai - 600127, Tamil Nadu, India; [email protected], [email protected] Keywords: Defense Mechanisms, Liveness Voice Detection, VoIP Cloud, Voice over Internet Protocol, VoIP Security Issues 1. Introduction The rapid progress of VoIP over traditional services is led to a situation that is common to many innovations and new technologies such as VoIP cloud and peer to peer services like Skype, Google Hangout etc. VoIP is the technology that supports sending voice (and video) over an Internet protocol-based network1,2. This is completely different than the public circuit-switched telephone net- work. Circuit switching network allocates resources to
- 67. each individual call and path is permanent throughout the call from start to end. Traditional telephony services are provided by the protocols/components such as SS7, T carriers, Plain Old Telephone Service (POTS), the Public Switch Telephone Network (PSTN), dial up, local loops and anything under International Telecommunication Union. IP networks are based on packet switching and each packet follows different path, has its own header and is forwarded separately by routers. VoIP network can be constructed in various ways by using both proprietary protocols and protocols based on open standards. 1.1 VoIP Layer Architecture VoIP communication system typically consist of a front end platform (soft-phone, PBX, gateway, call manager), back end platform (server, CPU, storage, memory, net- work) and intermediate platforms such as VoIP protocols, database, authentication server, web server, operating sys- tems etc. It is mainly divided into five layers as shown in Figure1. 1.2 VoIP Cloud Architecture VoIP cloud is the framework for delivering telephony services in which resources are retrieved from the cloud data center through web applications and soft-
- 68. ware, instead of a direct link to server3. Information and applications are stored on cloud servers in a distributed fashion. Apart from cloud computing characteristics such as on demand service, resource pooling, opti- mize resource allocation, pay as you go, elasticity and scalability4,5, VoIP cloud contains mainly six components as shown in Figure 2. *Author for correspondence Indian Journal of Science and Technology, Vol 9(6), DOI: 10.17485/ijst/2016/v9i6/81980, February 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645 A Comprehensive Survey of Security Issues and Defense Framework for VoIP Cloud Indian Journal of Science and Technology2 Vol 9 (6) | February 2016 | www.indjst.org 1.2.2 DHCP Server
- 69. It is used for dynamically distributing network configu- ration parameters such as Internet Protocol (IP) address, address of TFTP server etc. 1.2.3 Application Server These servers are designed to install, host and operate applications and provide services to end users, IT industries and organizations. 1.2.4 Time Server The main principle of time server is to maintain syn- chronization over the network. The actual time from server clock is distributed to its clients using a computer network. 1.2.5 TFTP Server It helps to update the network configuration used by the phones, routers, firewalls and perhaps provide a setting file that might contain operational parameters for VoIP network. e.g., software updates, codec used in a particular region. 1.2.6 Intrusion Prevention System (IPS) It monitors networks and systems behavior for malicious instances. The major roles of intrusion prevention sys-
- 70. tems are to find out suspicious instances and their log information, try to block/stop them and report to con- cern admin. 2. Literature Review VoIP technology was started in February 1995 by Vocaltec, Inc. in Israel. It transfers the voice over high speed network, cheaper comparing to PSTN and reach- able to everywhere through internet by loon developed by Google with 4G LTE speed6. 2.1 VoIP Security Issues VoIP transfers the voice over the data network through different network elements such as switches and rout- ers. Connecting PSTN to internet i.e. VoIP as a carrier for voice/video traffic, the security problems are not only common in circuit switch network (PSTN, POTS) such as eavesdropping (tapping) and toll fraud attack but also 15 42. Liao HJ, Lin CHR, Lin YC, Tung KY. Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications. 2013;
- 71. 36(1):16–24. 43. Audiopedia. Honeypot (computing). Available from: https://www.youtube.com/watch?v=2fXAw33jOBk. [Cited 2014 Dec]. 44. Goel R, Sardana A, Joshi RC. Wireless honeypot: framework, architectures and tools. International Journal of Network Security. 2013; 15(5):373–83. 45. Li Z, Grochulla M, Thormahlen T. Multiple active speaker localization based on audio- visual fusion in two stages. Proceedings IEEE International Conference on Multisensor Fusion Integration Intelligence Systems (MFI); Hamburg: Germany; 2012. p. 262–68. 46. Zhu ZY, He QH, Feng XH, Xiongli Y, Wang ZF. Liveness detection using time drift between lip movement and voice. Proceedings IEEE International Conference on Machine Learning Cybernetics (ICMLC); Tianjin: China; 2013. p. 973– 78. 47. Chetty G. Biometric liveness detection based on cross modal
- 72. fusion. IEEE 12th International Conference on Information Fusion (FUSION). Seattle: WA; 2009. p. 2255–62. Figure 1. VoIP layer architecture. Figure 1. VoIP layer architecture. 16 Figure 2. VoIP cloud architecture. Figure 3. Proposed VoIP cloud architecture.
- 73. Figure 4. Video watermarking scheme for signaling message. Figure 2. VoIP cloud architecture. 1.2.1 Call Server Phones are registered with this component. It handles security and admission control while connecting the phones. The Voice data of a call carried by the transport protocol may or may not flow through the call server. Ashutosh Satapathy and L. M. Jenila Livingston Indian Journal of Science and Technology 3Vol 9 (6) | February 2016 | www.indjst.org problems related to IP network. Security issues in VoIP are broadly classified into three categories. 2.1.1 Real Time Issues From last decade onwards, VoIP is used for several illegal activities such as hacking, terrorism, match fixing etc. Recently in October 2014, phone Hackers had broken
- 74. into the phone network of the company, Foreman Seeley Fountain Architecture and routed $166, 000 worth of calls from the firm to premium rate telephone numbers in Gambia, Somalia and Maldives. It would have taken 34 years for the firm to run of those charges legitimately, based on its typical phone bill. 2.1.2 Network Related Issues Attacks related to destroy, block, expose, alter, disable, steal or gain unauthorized access to information in VoIP network (e.g. threats include social, denial of service, ser- vice abuse, physical access, interruption of service etc.) are listed in Table 1 followed by different types of attacks7,8. 2.1.3 Voice Related Issues As VoIP system carries voice traffic, so victim’s voice can be mimicked by an attacker/intruder. A talking and sing- ing robot that mimics human vocalization, developed by M. Kitani, Kagawa University is vulnerable to VoIP communication9. 2.2 VoIP Attacks This section deals with different types of VoIP attacks. 2.2.1 Physical Attacks
- 75. The attacker performs this attack by stealing, breaking network equipment or direct control over equipment by getting unauthorized access to prohibited area for seeking of information. Some of the physical attacks are dumpster diving, shoulder surfing, hardware key logger and overt access etc. It can be prevented by keeping the documents and records safely inside locker and electronic equipment must be password protected. At last, outer layer security can be provided by deploying security guards at enter and exit points. 2.2.2 MAC Spoofing The technique of masking a MAC address upon actual MAC address through software emulation is known as MAC spoofing. Here the hacker’s system is taken over MAC address of one of the node which is already config- ured and permitted as VoIP end device by disconnecting or turning off it from rest of the network. It can be pre- vented by number of ways10. When ARP packet arrives, direct extraction of MAC address from LAN card and from OS registry; Compare the MAC address of LAN card with OS. If it doesn’t match, then delete the entry from OS registry. Lock down the system by registering its MAC address with a DHCP IP address. At last secure the
- 76. communication channel by encrypting it. 2.2.3 ARP Spoofing Hacker spreads forgery Address Resolution Protocol (ARP) packets inside VoIP network by modifying ARP buffer. Here, attacker binds own system MAC address with IP address of genuine server which causes the traffic imply for server is diverted to attacker. It advances hacker Table 1. VoIP network threats classification Threat Type Description Social threats These threats point straight against individuals such as misconfigurations, security holes or defective protocol implementation in VoIP system. (e.g., Phishing, Theft of identity or Service, Social engineering, Spam etc.) Eavesdropping, interception and modification
- 77. threats These threats include illegal/ Un- authorization access and modification of signaling and transport message. (e.g., Call rerouting, interception of RTP sessions etc.) Denial of service threats DoS threats repudiate individual access to VoIP services. DDOS attacks strike all of user’s or business transmission potentials. (e.g., SYN/UDP floods, ICMP floods, etc.) Service abuse threats These threats cause inappropriate utilization of VoIP services when those facilities are provided for business purposes. (e.g., toll fraud and billing avoidance etc.)
- 78. Physical access threats These threats are illegal physical access to VoIP devices or physical layer of the VoIP network. (e.g., Hardware key logger, theft of media, retrieval of discarded stuffs etc.) Interruption of services threats These threats cause VoIP services/ facilities to unviable and unavailable. (e.g., power loss due to bad climate, resource consumption due to over purchase/ extra subscription, issues that degenerate call quality etc.) A Comprehensive Survey of Security Issues and Defense Framework for VoIP Cloud Indian Journal of Science and Technology4 Vol 9 (6) | February 2016 | www.indjst.org
- 79. not only listen to VoIP calls but also reply and terminate the VoIP calls intended for other. ARP poisoning followed by denial service threats or eavesdropping, interception or modification threats which cause severe damages to vic- tim. So, Enhanced ARP can be implemented to prevent ARP spoofing11. 2.2.4 IP Spoofing Attacker gets into the VoIP network by tricking the IP address of any authorized machine which helps him to spread malicious message inside the network. IP spoofing helps attacker to launch further attacks such as DoS attack, theft of services, toll fraud etc. by impersonating autho- rized host inside VoIP network. Basically IP spoofing can be prevented with maximum probabilities by configuring broader gateway router. First, router disallows incom- ing packets for destination address coming from source address within one network. Second, router disallows to send packets from local network to another; those don’t have source addresses within that local address range. Y. Ma developed an effective trace route based method for counter measure against IP spoofing and it is worked with trusted adjacent nodes information i.e. acceptance of packets for a node is completely depends upon trace route
- 80. result from its adjacent nodes12. 2.2.5 ICMP Flood Internet Control Message Protocol (ICMP) is one of the network layer protocols that carry error and query mes- sages sent by either intermediate nodes or end node. Attacker tries to overflow the receiver cache by flood the respective node with ICMP packets. It forces the node to drop successive ICMP packets until free space available at node’s cache even if request packets come from genu- ine node. Routers are configured to set optimum points for traffic coming from different networks. It will help the routers to not only block unnecessary ICMP packets by matching ICMP requests and responses but also prevent cache overflow. The VoIP system must be configured sepa- rate VLAN for packets originating within a single network which are monitored by firewall. Barbhuiya et al. have developed an error detection framework to identify dif- ferent types of ICMP attack13. It consists of two modules. Verification module verifies origination of ICMP packets and Congestion check module extracts bandwidth utili- zation information using Simple Network Management Protocol (SNMP). 2.2.6 TCP/ UDP Floods
- 81. In TCP flooding attack, hacker creates huge number of SYN packets with abnormal source IP addresses and sends to receiver. Receiver node allocates space in its Transmission Control Buffer (TCB) to each SYN requests. In response to SYN packets, receiver sends SYN+ACK packets and waiting for ACK packets. The SYN+ACK packets carry abnormal IP addresses cause failure to receive ACK packets which prevents receiver node to clear TCP SYN requests from buffer and buffer to overflow later. Attacker can use TCP flood attack against VoIP signaling protocol such as H.323 and SIP; as both are connection oriented protocols. Haris et al. have succeed to detect TCP flood attack in communication by analyzing payload and unus- able area of the HTTP protocol (e.g., port, flags, source IP, header length)14. In UDP flood attack, large number of UDP packets are created with arbitrary source addresses and port num- bers and then sends to victim node. Receiver node will check whether any processes are running on those ports and find most of the ports are closed. In reply, receiver node creates large number of destination unreachable packets. Increase the number of ICMP packets causes the victim node and the network to overflow. The UDP flood attack prevents genuine nodes to communicate the
- 82. victim node at a particular span of time. Attacker can use UDP flood attack against VoIP transport protocol such as RTP and RTCP; as both are connection less pro- tocol. Bardas et al. proposed a proportional packet rate assumption technique to differentiate UDP traffic for detecting forge IP addresses responsible for UDP flood attacks15. 2.2.7 TCP/ UDP Replay First, attacker tries to obtain network sensitive information such as session cookies, password, voice data, signal- ing data. The information captured by sniffing tools can be used by attacker to take over the ongoing session. Sometime victim’s voice can be impersonated by directly playing back recorded voice data or slightly modifying voice data and send to destination which helps the hacker to retrieve more information between caller and callee. Encrypt the sessions is the best way to stop penetration. Ali et al. proposed an enhanced port knocking technique to block TCP replay and port scanning attacks16. It is worked on source port sequences authentication instead of destination port sequence number.
- 83. Ashutosh Satapathy and L. M. Jenila Livingston Indian Journal of Science and Technology 5Vol 9 (6) | February 2016 | www.indjst.org 2.2.8 SIP Registration Hijacking VoIP phones use SIP or other signaling protocols to register own MAC and IP addresses with call server. In the reply, each phone will get unique call ID which allows it to make or receive VoIP call. Attacker tries to capture registration packets and replaces MAC address from the packets with own MAC address. It helps the rogue node to register with victim IP address which causes call intending for victim node will be forwarded to attacker. SIP registration hijacking allows burglars to track, block and manipulate voice traffic. As end node registration is based on TCP connection, attack will be prevented by implementing SSL/TLS security policies 17. 2.2.9 Malformed Packets The hacker creates malicious packets and forwards them to nodes inside VoIP networks with the help of networking protocols. The target node processes those packets, causes open unnecessary ports and processes which degrade per- formance of the nodes to handle VoIP traffic. New patches
- 84. and software will be installed to maintain the node up-to- date and shutdown the security holes which are vulnerable to attack. New generation firewalls must be installed to provide protection against vulnerable packets by filtering packets based on inbound rules, outbound rules and con- nection security rules. Geneiatakis et al. have succeeded in developing a framework that provides defense against malformed packets for VoIP infrastructure18. The detec- tion mechanism is based on signature detection which consists of two parts. First one, general signature detec- tion (e.g., SIP METHOD, SIP URI, HEADERS) applicable to all the packets and second one is method specific (e.g., CALL-ID, Content-Type, INVITE _METHOD) differ from packets to packets. 2.2.10 SIP Message Modification In message modification attack, by running network sniffing tools (e.g.,Wireshark), attacker penetrates traffic and tries to modify signaling message for better control over the VoIP network. Suppose a user initiates a call to victim’s phone by sending SIP message to call server. Modification of SIP messages confuses and forces the server to connect rogue phone. User knows that he is connected to one user but actually the traffic is routed to attacker. SIP message modification is carried out by performing MITM attack
- 85. such as MAC spoofing, IP spoofing or ARP poisoning. As SIP and RTP packets transmission are taken place over TCP and UDP connection; VoIP traffic must be encrypted by implementing SSL/TLS to prevent this attack17. 2.2.11 SIP Cancel/ Bye Attack Host (zombie) must be configured in promiscuous mode to lunch attack into VoIP network by sending SIP Cancel or Bye packets. Abnormal packets are created and sent to an IP phone from its connected IP phone by spoofing its IP address which will proceed to terminate the ongo- ing call. Attacker can perform this attack continuously for certain period of time by spoofing more than one IP addresses which causes denial of service attack. As both signaling and transport protocols use no authentication prior to data transmission, so, this attack can be prevented by encrypt the communication channels. Second, provide authentication between end device and call server and at last verification of authenticity of signaling message by end devices before processing 19. 2.2.12 SIP Malformed Command In web based VoIP communication (e.g. Facebook, Google Hangout), Hyper Text Markup Language (HTML)
- 86. plays a major role as it carries all the signaling informa- tion/ command in its body. Parsing SIP command within HTML code for all possible input is really a headache. Attacker tries to inject malformed SIP command in input field and send to server for processing as like SQL injec- tion. In response either it breaks the server authentication or degrades the performance of server and end devices. In counter measure, whether packets are coming from genuine user or not will be confirmed by call server by verifying authenticity of SIP message before processing. Dictionary and fuzzy tests must be performed on HTML code that filtered tricky SIP malformed packets used to exploit server. M. Su and C. Tsai propose two functions to resists malformed SIP packets and flooding attack on call servers20. First function filters malformed packets and second one uses Chi-square test to measure flooding attack on SIP server. 2.2.13 SIP Redirect Call server cache maintains data structure of Phone’s caller ID, corresponding MAC and IP address. Attacker manipulates call server cache to confuse the call server for call redirection. So, SIP packets coming for receiver are redirected to attacker specified number. Attacker can perform DoS and DDoS attack by redirecting a single call
- 87. A Comprehensive Survey of Security Issues and Defense Framework for VoIP Cloud Indian Journal of Science and Technology6 Vol 9 (6) | February 2016 | www.indjst.org or all the calls to void device(s). So, call server must be strong password protected and SIP must be authenticated to prevent redirection attack19. 2.2.14 RTP Payload Captured packets will be played later to listening the conversation between the end users using sniffing tools. Attacker can insert own voice inside RTP payload which degrade the quality of conversation and some- time changed in the meaning of conversation. In RTP tampering, header fields (sequence number, synchroni- zation source Identifier, payload type, timestamp etc.) are tampered which make the packets either unusable or delayed, causes rejection at receiver end. In RTP redi- rection, header field of packets are modified with other receiver caller id and IP address causes packets intending
- 88. for one will go to other. It can be prevented by configuring VoIP network with Secure Real-Time Transport Protocol (SRTP) instead of RTP21. It will encrypt the RTP packets propagate between callers. 2.2.15 Buffer Overflow Buffer is the temporary storage allocated by OS in physical memory for processing data by computer program. Buffer is mainly divided into four types such as code, data, stack and heap segments. Attacker tries to perform buffer over- flow attack by targeting at least one of the segments. It helps to steal or modify the sensitive information or install malicious code and execute it. Buffer overflow attacks are mainly executed by four ways such as long jump, function activation record, pointer subterfuge and malicious code execution. It can be defended by writing secure code, performing bound checking or static and dynamic code analysis and runtime code instrumentation22. 2.2.16 Operating System In VoIP communication network, IP phones, Call server, TFTP server, gateway and DHCP server etc. requires OS (e.g., Windows, Linux, Mac) to run. So, vulnerabili- ties in OS make them vulnerable23. OS vulnerabilities in VoIP phones are mainly of two types. Hard phones
- 89. have in build embedded OS which is less vulnerable and more protected than soft phones. VoIP soft phones are software packages which are installed on computers connected to data network. Old hardware, unsupported drivers, bad integration of APIs, unsecure administrator APIs expose OS to attack. Like IP phones, web server OS, DHCP server, and call manager can be exploited by attacker for seeking of sensitive and crucial infor- mation (e.g., password, IP table, VoIP configuration file). As default configuration of OS is not secure, it is exposed to malwares to install. Its execution opens well known ports which helps attacker to run abnormal pro- cesses (e.g., free call, toll fraud). It can be pre-empted by hardening OS24. 2.2.17 Malwares A vulnerable piece of executable codes or program used by unknown third party to install in VoIP network and bring down its performance by hook or crook. Malicious programs or malwares are mainly classified as two cat- egories, first one simple malwares and second one is self-replicated malwares25. Logic bomb and Trojan horse are come under non self-replicated/simple malware. Self- replicating malware such as virus and worm, who spread
- 90. its infection over the network within few hours or days. Trojan horses are dispatched over network for remote control over victim VoIP phones. Logic bomb helps the attacker to trigger other dangerous attacks (DoS, DDoS, sniffing etc.) in timely manner. It will be prevented by installing updated antivirus and patching up VoIP system software on regular basis. 2.2.18 Application Flaws As most of the VoIP communications are web based, it’s vulnerable to two major application flaws such as Structured Query Language (SQL) Injection attack and cross site scripting attacks. In SQL Injection attack, mali- cious commands are inserted in SQL statements to gain unauthorized access to server database. It can be prevented by implementing three primary defense mechanisms such as defensive coding, SQL injection vulnerabilities detection and runtime SQL injection attack prevention26. In cross site scripting attack, hacker uses the advantages of scripting languages to launch attack by injecting mali- cious code inside the web application. It can be prevented by configuring strong authentication and validation for web based VoIP application27. 2.2.19 TFTP Server Insertion
- 91. Hacker tries to plant rouge TFTP server in the network by disabling/ spoofing actual TFTP server. It forces IP phones to receive wrong configuration information (e.g., Call ID, SIP server IP address and phone number) which Ashutosh Satapathy and L. M. Jenila Livingston Indian Journal of Science and Technology 7Vol 9 (6) | February 2016 | www.indjst.org may provoke bill fraud attack. It will be prevented by encrypting and authenticating the channel between IP phones and TFTP server using TLS/ SSL. N. N. Mohamed et al. suggested compression and encryption technique to secure TFTP packets28. For compression, lossless algorithm (e.g., Huffman coding) and for encryption, symmetric encryption algorithm (e.g., AES, 3-DES) is used. Diffie-Hellman Key Exchange algorithm is used for distribution of symmetric key between client and server. 2.2.20 DHCP Server Starvation Attacker generates random MAC addresses and creates DHCP request for each MAC address. By flooding DHCP
- 92. server with DHCP requests, consumes DHCP IP pool and to overflow later. It is to be continued until reserved IP addresses DHCP timers will be expired. Dinu and Togan proposed digital certificate based DHCP server authentication to stop DHCP server starvation attack29. It uses asymmetric key cryptography and digital certificates for DHCP server authentication and verifying DHCP response from it to prevent starvation. 2.3 Defense Mechanisms to Prevent Attacks Defense mechanisms provide basic counter measures to prevent potential VoIP attacks explained above are broadly classified into twelve types and listed in Table 2 7,30. 2.3.1 Physical Access Control (PAC) Physical securities can be implemented mainly three ways31. First, equipment should be placed and surrounded by multi-layer barriers, which will prevent from natu- ral disasters like cyclone, floods etc. (e.g., wall, multiple locks, fireproof safes etc.). Second, deployment of surveil- lance systems such as smoke and heat detectors, cameras, alarms that decreases occurrences of manmade disasters with maximum amount. At last, practices must be imple- mented to prevent before any attack has been occur and fast recovery from damages, if any attack has occurred.
- 93. 2.3.2 ARP Cache Protection (ACP) Static ARP cache entries allow maintaining manual mapping between IP address to MAC address so that Table 2. Defense mechanisms against attacks Attacks Defense Mechanisms 1. Ph ys ic al A tt ac ks
- 105. se rv er S ta rv at io n PAC √ ACP √ OSP √ √ √ √ PA √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ RC √ √ FC √ √ √
- 106. SVDT √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ CA √ √ SA √ √ √ √ √ √ √ ME √ IDS √ √ √ √ √ √ √ √ √ √ √ √ √ √ √ Honeypot √ √ A Comprehensive Survey of Security Issues and Defense Framework for VoIP Cloud Indian Journal of Science and Technology8 Vol 9 (6) | February 2016 | www.indjst.org reply packets are discarded. ARP anti-spoofing software ignores ARP spoofing packets by it certification or crosschecking of its responses. It can be integrated with Dynamic Host Configuration Protocol (DHCP) server, so
- 107. that all static and dynamic IP addresses are certified before used. Operating system security is provided by configur- ing its registry files that prevent burglar to update ARP cache. Registries files are found under HKEY_LOCAL_ MACHINESYSTEM folder. Yang, Yang and Ding proposed a WinPcap driven system that monitors all ARP packets for ARP spoofing32. WinPcap driver captures and monitors the packets to verify whether the IP-MAC map- ping is legal or not by checking packets coming from legal hosts, before update the cache. 2.3.3 Operating System (OS) Protection (OSP) OS protection requires vulnerability assessment and management techniques such as patching OS holes, OS hardening, updating security software, OS auditing, proper priviligation to user accounts etc. Kaczmarek and Wrobel proposed integrity checking and recovery (ICAR) protec- tion model comprises of three layers and it’s responsible for hash creation, verification and configuration of secu- rity policies33. Data layer consists of sensitive information and database that contains hashes and backup of highly sensitive information. Kernel layer manages verification of data integrity, authenticity and confidentiality. Utility layer is responsible for configuring security policies and controlling the host.
- 108. 2.3.4 Port Authentication (PA) Strong port authentication can provide defense against interception, interruption and modification of traffic, interoperability between old and new network protocols and prevent malicious software execution. IEEE 802.1X port based authentication supplies security credentials such as user id, password or digital certificate to legal user34. User has to use the credential for verification, before access the resources. If server verifies the creden- tials are valid, user is permitted to utilize the resources. deGraaf, Aycock and Jacobson explained port knocking where authentication data is communicated throughout network ports to prevent unauthorized access35. 2.3.5 Router Configuration (RC) Fraudulent route update packets are blocked by configuring neighbor configuration. The configuration is available in the following routing protocols such as Boarder Gateway Protocol (BGP), DRP Server Agent, Intermediate System-Intermediate System (IS-IS) etc. Sehgal and Nath proposed secure routing proto- col which has two phases36. In route discovery phase, Source node initiates a request to discover a route from
- 109. source to destination. Route unitization phase, source selects one of the route and the destination has to con- firm that route by sending reply packet to source. Source sends predecessor packet to notify intermediate nodes on the route that they should anticipate certain amount of data within a given time. When this packet reaches at destination, source receives an acknowledgement from destination. If not, there is a malicious user in between the path. 2.3.6 Firewall Configuration (FC) Firewalls mainly configured into three types such as packet filters, stateful inspection and application proxy. Packet filter monitors all the packets header fields such as source and destination address, port number and pro- tocol used based on predefined rules. Stateful inspection firewall tracks network connection state and differentiate packets based on the type of connection. It is also called dynamic packet filtering firewall and its fail to examine the content of the payload. Application proxy firewall allows the entire traffic pass through a proxy server, which verifies packets header including its content at application level for any malicious activities. Chacon, Benhaddou and Gurkan proposed Virtual Private Network (VPN) based firewall that provides more security to Boarder Gateway
- 110. Router (BGR) by making voice information less vulner- able to both inside and outside attack37. 2.3.7 Separate VoIP Data Traffic (SVDT) In VoIP communication, both voice and data share common medium for transmission which raises threats against voice. Butcher, Li and Guo proposed separation of voice from normal data flow can block a number of attacks which are vulnerable to FTP, HTTP and SMTP etc. 7. Separate physical network causes more expense, so, it can be possible through VLAN technology. VLANs are implemented by network switches allow routing on same VLAN between devices. Both VoIP voice and data are segmented using firewall where PCs are connected to data segment and VoIP phone are connected to voice segment. Ashutosh Satapathy and L. M. Jenila Livingston Indian Journal of Science and Technology 9Vol 9 (6) | February 2016 | www.indjst.org 2.3.8 Configuration Authentication (CA)
- 111. In VoIP telephony network obtaining the configuration information from unwanted vulnerable server makes end phones more sensitive to launch attacks. Danforth and Gould described different ways of authenticating TFTP server before downloading configuration file38. During manufacturing time, the VoIP phones are configured with public keys of different TFTP servers gives a way for authentication. Another way the handsets are configured with a key (public or secret) of TFTP server. After getting own IP address and TFTP server IP address from DHCP server, phone should establish a secure connection with TFTP server using SSL/TLS. During handshaking, verifi- cation happens using public key that phone contains and private key of TFTP server. 2.3.9 Signaling Authentication (SA) In VoIP network, SIP is used to establish, redirect or terminate the connection. Internet Protocol Security (IPSec) and SSL/TLS are used to provide strong authen- tication and encryption against attack. Key agreement protocol is used in small scale deployment where trust being established between phones and server. Kilinc and Yanik presented different authentication and key management schemes for SIP protocols which mainly include Password Authenticated Key Exchange (PAKE)
- 112. based schemes, Hash and Symmetric Encryption based schemes, Public Key Cryptography (PKC) schemes and ID Based and Weil Pairing based schemes39. 2.3.10 Medium Encryption (ME) In VoIP, medium encryption is broadly classified into two types such as symmetric and asymmetric encryption40. Encryption strength is mainly depending upon the strength of the algorithm and the size of key is used. In encryption techniques key management and distribution also play an important role41. To maintain confidentiality and integrity, symmetric key is distributed with the help of the techniques such as simple secret key distribution, secret key distribu- tion with confidentiality and authentication and hybrid key distribution. Public key sharing has taken place using public announcement, publicly available directory, public key authority or public key certificate techniques. 2.3.11 Intrusion Detection System (IDS) Hardware/ software are used to monitor network traffic for malicious and unlawful actions and notify to admin by warning message or raising alarm. Basically IDS is of two types such as Network Intrusion Detection System and Host Intrusion Detection System. It commonly uses three
- 113. methodologies to track down malicious activities. Signature based detection is the valuable and straight forward meth- ods to uncover known threats. Anomaly based detection is effective one to identify new and expected threats. Stateful protocol analysis tracks down protocols performance and differentiates abnormal flow of commands. All these three methods use five different approaches such as statistics based, pattern based, rule based, state based and heuristic based which were discussed by Liao, Lin, Lin and Tung42. 2.3.12 Honeypot It is a trap set to detect, deflect or counter attempt at unau- thorized use of information system which is seems to be contain of information or resources of values to attackers. It is classified based on their deployment and based on their level of involvement43. Based on its deployment it’s of two types, one is production honeypot and second one is research honeypot. According to level of involvement or design perspective, pure honeypot, higher interaction honeypots, low interaction honeypots are three types of honeypots. Goel, Sardana and Joshi presented a wide range of honeypot systems and proposed framework for honeypot system that enclose a broad range of honeypot architectures and categories previous systems according to framework based on attacks44.
- 114. 3. Proposed Work VoIP cloud with all security configurations as discuss earlier is not enough to provide security against all the threats discussed earlier. So, effective network based IPS architecture using LVD technique is proposed for VoIP cloud shown in Figure 3. The proposed system involves twosteps process. 1) Video watermarking and extraction and 2) Verification using LVD system 3.1 Video Watermarking and Extraction There are several VoIP-specific protocols but they fall in two categories: (i) transport protocols (e.g., RTP) and (ii) signaling protocols (e.g., SIP). Transport protocols carry the live video data after the proper the connection. Signaling protocols executes control information like CONNECT, DISCONNECT etc. A Comprehensive Survey of Security Issues and Defense Framework for VoIP Cloud
- 115. Indian Journal of Science and Technology10 Vol 9 (6) | February 2016 | www.indjst.org Sequence numbers play important roles to prevent SSL attacks in real time communication. So at the first step the sequence number is attached with the video/ frames taken during the connection/disconnection time. Since the control information is not a video data, it has to be embedded with video using watermark embedding tech- nique and forwarded to the server side for authentication as illustrated in Figure 4. In the server side, the water- marked video will be extracted and the video submitted to the LVD for verification. IPS in VoIP cloud not only monitors the packets are coming from legitimate caller or not but also moni- tors sequence number of packets and its content. In real time communication succeeding packet sequence num- ber always larger than current packet sequence number and contents varies packet to packet and time to time which helps IPS to prevent SSL attacks to be taken place. If any packet with old sequence number or repetition of information inside the payload, it will be dropped by VoIP server. Always signaling packets will get high priority over
- 116. data packets. 3.2 Verification using LVD System The aim of the LVD is to determine if the biometric data is being captured whether it’s from a legitimate live user or it is replayed or synthetic. It is verified by correlating the user’s voice with lip-face motion. The Whole LVD is divided into seven steps. The whole process of verifica- tion is divided into two stages. The flow diagram of LVD is shown in Figure 5. There are two types of videos passed to LVD system for verification. 1) Extracted watermarked video2) RTP message along with the video. 3.2.1 Seven Steps of LVD Step 1: Image and Voice Acquisition: Video and Voice are captured by web cam and microphone respec- tively. Step 2: Image and Speech Enhancement: The captured information goes for reduction of noise, and this can be achieved by smoothing and sharpening the audio- visual data.
- 117. 16 Figure 2. VoIP cloud architecture. Figure 3. Proposed VoIP cloud architecture. Figure 4. Video watermarking scheme for signaling message. Figure 4. Video watermarking scheme for signaling message. 17 Figure 5. Flow diagram of liveness voice detection technique.
- 118. Figure 6. Proposed N-IPS architecture. Figure 5. Flow diagram of liveness voice detection technique. 16 Figure 2. VoIP cloud architecture. Figure 3. Proposed VoIP cloud architecture. Figure 4. Video watermarking scheme for signaling message. Figure 3. Proposed VoIP cloud architecture. Ashutosh Satapathy and L. M. Jenila Livingston
- 119. Indian Journal of Science and Technology 11Vol 9 (6) | February 2016 | www.indjst.org Step 3: Face and Voice Identification: Real time faces detection and speech detection can be done by motion analysis. Two level audio- visual fusion techniques can be used for effective face and voice identification by localization of multiple active speakers45. First level is based on speaker activity detection used to find out who are the live speak- ers and second level uses Gaussian method for integration of audio-visual modalities results to increase robustness. Step 4: Facial Verification and Speaker Recognition: In Face verification and speaker recognition process, user authenticity is checked. If NO, the call is termi- nated. If yes, it will go for next level verification. Step 5: Features Extraction: From lip movement, the motion based features will be extracted (Phase 1). From speech, the corresponding speech features will be extracted (Phase 2). Extracted features at both phases must be language and text indepen-
- 120. dent. Lip movement involves the following two elements. Fastness: frequency is the prime factor calculated from lip motion. Loudness: Power is the prime factor calculated from area covered by lips (distance between top and bottom lip). Step 6: Liveness Voice Detection: Features from phase 1 is correlated with features from phase 2. If it falls within fixed threshold values range, it is Ok, the connection will be continued else it will be terminated. In LVD, liveness score evolution algorithm can be used to measure the synchrony between the lip movement and voice in video sequence46. Multimodal sys- tem based on cross modal fusion technique can be also used for liveness detection47. Audio and visual speech features are extracted from video sequence to measure the degree of synchrony between the lip movement and voice in video sequence. 3.3 IPS Deployment As VoIP communication handles real time data, IPS deployment is also an important factor to make the service more effective and efficient. In pass-by monitoring con- figuration, a copy of the traffic is sent to the IPS while the original packet travels to one Public Network to another
- 121. public network as shown in Figure 6. If the IPS identifies an anomaly with the packet, the IDS/IPS can either log/ record the activity or prevent the attack from being suc- cessful. As an effective IPS, false positive/ negative should have minimized with maximum efficiency. 4. Conclusion The flexible and reliable communication is reached by sending the voice over the internet by using new gen- eration VoIP services. This research paper summarizes security threats related to VoIP cloud. Due to VoIP vulnerabilities including eavesdropping, DoS, D-DoS, MITM attack, it is necessary to protect signaling and real time information. A security framework for VoIP cloud is proposed which uses the concept of LVD to provide resistance against threats. The use of pass-by IPS makes this proposed framework has no effect on quality of VoIP calls as the copy of the original packets are forwarded to IPS and analyzed. At last, further research has to be per- formed to raise the level of security due to randomness of occurrence of cyber-attacks. 5. References 1. Hartpence B. Introduction to voice over the internet
- 122. protocol. Packet Guide to Voice over IP. Oram A, Gulick M, editors. O’Reilly: Sebastopol, CA; 2013. 2. Devi GU, Kaushik KV, Sreeveer B, Prasad KS. VoIP over Mobile Wi-Fi hotspot. Indian Journal of Science and Technology. 2015 Jan; 8(S2):195–9. DOI: 10.17485/ ijst/2015/v8iS2/58751. 3. Patinge SA, Soni PD. Metamorphosis in VoIP cloud com- puting services used in VoIP. International Journal of Application Innovation in Engineering Management. 2013; 2(2):236–9. 4. Mahmood Z. Cloud computing: characteristics and deploy- ment approaches. 11th IEEE International Conference Computer and Information Technology (CIT); Pafos: Cyprus; 2011. p. 121–6. 5. Shyamala K, Rani TS. An analysis on efficient resource allocation mechanisms in cloud computing. Indian Journal Figure 6. Proposed N-IPS architecture. 17
- 123. Figure 5. Flow diagram of liveness voice detection technique. Figure 6. Proposed N-IPS architecture. A Comprehensive Survey of Security Issues and Defense Framework for VoIP Cloud Indian Journal of Science and Technology12 Vol 9 (6) | February 2016 | www.indjst.org of Science and Technology. 2015 May; 8(9):814–21. DOI: 10.17485/ijst/2015/v8i9/50180. 6. Kim D. A survey of balloon networking applications and technologies. Available from: http://www.cse.wustl. edu/~jain/cse570-13/ftp/balloon/index.html. [Cited 2014
- 124. Aug]. 7. Butcher D, Li X, Guo J. Security challenge and defense in VoIP infrastructures. IEEE Transactions on Systems Man and Cybernetics Part C: Applications Reviews. 2007; 37(6):1152–62. 8. Graves K. Certified ethical hacker study guide, 4th ed. Wiley: Danvers, MA; 2010. 9. Sawada H, Higashimoto T. A mechanical voice system and its adaptive learning for the mimicry of human vocal- ization. Proceedings IEEE International Symposium on Computational Intelligence Robotics and Automation; Cobe, Japan; 2003. p. 1040–45. 10. Hatkar AA, Varade GA, Hatkar AP. Media access control spoofing techniques and counter measures. International Journal Scientific & Engineering Research. 2012; 2(6):1–5 . 11. Nam SY, Kim D, Kim J. Enhanced ARP: preventing ARP poisoning-based Man-in-the-Middle Attacks. IEEE Communications Letters. 2010; 14(2):187–9. 12. Ma Y. An effective method for defense against IP spoof-
- 125. ing attack. IEEE 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM); Chengdu: China; 2010. p. 1–4. 13. Barbhuiya FA, Roopa S, Ratti R, Biswas S, Nandi S. An active detection mechanism for detecting ICMP based attacks. IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications; Liverpool: England; 2012. p. 51–58. 14. Haris SHC, Ahmad RB, Ghani MAHA, Wal GM. TCP SYN flood detection based on payload analysis. Proceedings IEEE Student Conference on Research and Development (SCOReD); Putrajaya: Malasia; 2010. p. 149–53. 15. Bardas AG, Zomlot L, Sundaramurthy SC. Classification of UDP traffic for DDoS detection. USENIX 5th International Workshop on Large-Scale Exploits and Emergent Threats (LEET); San Jose: CA; 2012. p. 1–8. 16. Ali FHM, Yunos R, Alias MAM. Simple port knocking method against TCP replay attack and port scanning. IEEE International Conference on Cyber Security. Cyber Warfare and Digital Forensic (CyberSec); Kuala Lumpur: Malasia;
- 126. 2012. p. 247–52. 17. Stalling W. Transport-level security. Cryptography and Network Security. Horton M, editor, 5th ed., Pearson: Upper Saddle River, NJ; 2011. p. 485–20. 18. Geneiatakis D, Kambourakis G, Lambrinoudakis C, Dagiuklas T, Gritzalis S. A frame for protecting a SIP-based infrastructure against malformed message attacks. Computer Network. 2007; 51(10):2580–93. 19. Zhang G, Pallares JJ, Rebahi Y, Fischer-Hubner S. SIP proxies: New reflectors in the internet. Communications Multimedia Security; Springer : Verlag Heidelberg; 2010. 20. SuM Y, Tsai CH. An approach to resisting malformed and flooding attacks on SIP servers. Journal of Networks. 2015; 10(2):77–84. 21. Hartpence B. The real-time transport protocol and the real-time control protocol. Packet Guide to Voice over IP, Oram A, Gulik M, editors, 1st ed.; O’Reilly: Sebastopol, CA; 2013.
- 127. 22. Fu D, Shi F. Buffer overflow exploit and defensive tech- niques. IEEE International Conference on Multimedia Information Networking and Security (MINES); Nanjing, China; 2012. p. 87–90. 23. Ransome JF, Rittinghouse JR. VoIP security risks. VoIP Security, Casey E, editor; Elsevier: Burlington, MA; 2005. 24. Hardening the operating system. Available from: http://cdn. ttgtmedia.com/searchEnterpriseLinux/downloads/466_ HTC_Linux_02.pdf. [Citied 2014 Oct]. 25. Filiol E. Taxonomy, techniques and tools. Computer Viruses: From Theory to Applications, 1st ed.; Springer: Verlag, France; 2004. 26. Shar LK, Tan HBK. Defeating SQL injection. IEEE Computer: Gender Diversity in Computing. 2013; 46(3):69–77. 27. Natan RB. Application security. Implementing Database Security and Auditing; Elsevier: Burlington, MA; 2005. 28. Mohamed NN, Mashim H, Yussoff YM. Compression and encryption technique on securing TFTP packet.
- 128. IEEE Symposium on Computer Application Industrial Electronics (ISCAIE); Penang, Malaysia; 2014. p. 198–202. 29. Dinu DD, Togan M. DHCP server authentication using digital certificates. Proceedings IEEE 10th International Conference Communications (COMM); Bucharest, Romania; 2014. p. 1–6. 30. Keromytis AD. A comprehensive survey of Voice over IP security research. IEEE Communications Surveys & Tutorials. 2012; 14(2):514–37. 31. Graves K. Physical site security. Certified Ethical Hacker Study Guide, Parsons K, Carson C, 4th ed.; Wiley: Danvers, MA; 2010. 32. Yang M, WangY, Ding H. Design of WinPcap based ARP spoofing defense system. IEEE 4th International Conference on Instrumentation Measurement Computer, Communication Control (IMCCC); Harbin, Heilongjiang; 2014. p. 221–5. 33. Kaczmarek J, Wrobel MR. Operating system security by integrity checking and recovery using write-protected storage. IET Information Security. 2014; 8(2):122–31.
- 129. Ashutosh Satapathy and L. M. Jenila Livingston Indian Journal of Science and Technology 13Vol 9 (6) | February 2016 | www.indjst.org 34. IEEE 802.1X port-based authentication, CISCO. Available from: http://www.cisco.com/c/en/us/td/docs/switches/lan/ catalyst6500/ios/122SX/configuration/guide/book/dot1x. html#wp1133592. [Cited 2015 Jan]. 35. deGraaf R, Aycock J, Jacobson M. Improved port knocking with strong authentication. Proceeding. IEEE 21st Annual Computer Security Applications Conference (ACSAC); Tucson, Arizona; 2005. p. 453–62. 36. Sehgal PK, Nath R. An encryption based dynamic and secure routing protocol for mobile Ad-hoc network. International Journal of Computer Science Security. 2009; 3(1):16–22. 37. Chacon S, Benhaddou D, Gurkan D. Secure Voice over Internet Protocol (VoIP) using Virtual Private Networks
- 130. (VPN) and Internet Protocol Security (IPSec). IEEE Region 5 Tech. Professional and Student Conference (TPSC); San Antonio: TX; 2006. p. 218–22. 38. Danforth A, Gould K. Method to block unauthorized access to TFTP server configuration files, U.S. Patent 7293282 B2, 2007. 39. Kilinc HH, Yanik T. A survey of SIP authentication and key agreement schemes. IEEE Communications Survey and Tutorials. 2014;16(2):1005–23. 40. Anderson R. Cryptography. Security Engineering: A Guide to Building Dependable Distributed Systems, Long C, 2nd ed., Wiley: Indianapolis, IN; 2008. p. 73–14. 41. Stalling W. Key management and distribution. Cryptography and Network Security. Horton M, 5th ed., Pearson: Upper Saddle River, NJ; 2011. p. 410–43. 42. Liao HJ, Lin CHR, Lin YC, Tung KY. Intrusion detection system: A comprehensive review. Journal of Network and Computer Applications. 2013; 36(1):16–24. 43. Audiopedia. Honeypot (computing). Available from:
- 131. https://www.youtube.com/watch?v=2fXAw33jOBk. [Cited 2014 Dec]. 44. Goel R, Sardana A, Joshi RC. Wireless honeypot: frame- work, architectures and tools. International Journal of Network Security. 2013; 15(5):373–83. 45. Li Z, Grochulla M, Thormahlen T. Multiple active speaker localization based on audio-visual fusion in two stages. Proceedings IEEE International Conference on Multisensor Fusion Integration Intelligence Systems (MFI); Hamburg: Germany; 2012. p. 262–68. 46. Zhu ZY, He QH, Feng XH, Xiongli Y, Wang ZF. Liveness detection using time drift between lip movement and voice. Proceedings IEEE International Conference on Machine Learning Cybernetics (ICMLC); Tianjin: China; 2013. p. 973–78. 47. Chetty G. Biometric liveness detection based on cross modal fusion. IEEE 12th International Conference on Information Fusion (FUSION). Seattle: WA; 2009. p. 2255–62.