BIOMETRIC SMARTCARD AUTHENTICATION FOR FOG COMPUTING

International Journal of Network Security & Its Applications (IJNSA) Vol. 10, No.6, November 2018
DOI: 10.5121/ijnsa.2018.10604 35
BIOMETRIC SMARTCARD AUTHENTICATION FOR
FOG COMPUTING
Kashif Munir and Lawan A. Mohammed
University of Hafr Al Batin, KSA
ABSTRACT:
In the IoT scenario, things at the edge can create significantly large amounts of data. Fog Computing has
recently emerged as the paradigm to address the needs of edge computing in the Internet of Things (IoT)
and Industrial Internet of Things (IIoT) applications. In a Fog Computing environment, much of the
processing would take place closer to the edge in a router device, rather than having to be transmitted to
the Fog. Authentication is an important issue for the security of fog computing since services are offered to
massive-scale end users by front fog nodes.Fog computing faces new security and privacy challenges
besides those inherited from cloud computing. Authentication helps to ensure and confirms a user's
identity. The existing traditional password authentication does not provide enough security for the data and
there have been instances when the password-based authentication has been manipulated to gain access
into the data. Since the conventional methods such as passwords do not serve the purpose of data security,
research worksare focused on biometric user authentication in fog computing environment. In this paper,
we present biometric smartcard authentication to protect the fog computing environment.
KEYWORDS:
Biometric Authentication, Fog Computing, Security
1. INTRODUCTION
Fog computing, also known as fogging/edge computing, is a model in which data, processing,and
applications are concentrated in devices at the network edge rather than existing almost entirely in
the fog as per Cisco [6]. The concentration means that data can be processed locally in smart
devices rather than being sent to the fog for processing. As per [26], Fog computing is one
approach to dealing with the demands of the ever-increasing number of Internet-connected
devices sometimes referred to as IoT. Cisco recently delivered the vision of fog computing to run
applications on connected devices that would run directly at the network edge. Customers can
develop, manage, and run software applications on the Cisco framework of the networked
devices. This includes the difficult routes and switches. Cisco brought this new innovation where
they combined the open-source Linux and network operating system together in a single network
device.
According to [3], fog computing is considered as an extension of the cloud computing to the edge
of the network, which is a highly virtualized platform of the resource pool that provides
computation, storage, and networking services to nearby end users. As per [23], fog computing as
“a scenario where a huge number of heterogeneous (wireless and sometimes autonomous)
ubiquitous and decentralized devices communicate and potentially cooperate among them and
with the network to perform storage and processing tasks without theintervention of third parties.
These tasks can beused for supporting basic network functions or new services and applications

36
that run in a sandboxed environment. Users leasing part of their devices to host these services get
incentives for doing so.” Although those definitions are still debatable before, fog computing is
no longer a buzzword.
According to [4],Fog model provides benefits in advertising, computing, entertainment, and other
applications, well positioned for data analytics and distributed data collection points. End services
like, set-up-boxes and access points can be easily hosted using fogging. It improves QoS and
reduces latency. The main task of fogging is positioning information near to the user at the
network edge. In general, some of the major benefits of fog computing are:
 The significant reduction in data movement across the network resulting in reduced
congestion, cost and latency, elimination of bottlenecks resulting from centralized
computing systems, improved security of encrypted data as it stays closer to the end user
reducing exposure to hostile elements and improved scalability arising from virtualized
systems.
 Eliminates the core computing environment, thereby reducing a major block and a point
of failure.
 Improves the security, as data are encoded as it is moved towards the network edge.
 Edge Computing, in addition to providingthe sub-second response to end users, it also
provides high levels of scalability, reliability and fault tolerance.
 Consumes less amount of bandwidth.
The OpenFog consortium released the OpenFog reference architecture (RA) recommendations for
anyone wishing to implement fog computing or any fog-based applications. The OpenFog
Reference Architecture is based on eight core technical principles, termed pillars, which represent
the key attributes that a system needs to encompass to be defined as “OpenFog.” These pillars
include security, scalability, openness, autonomy, RAS (reliability, availability, and
serviceability), agility, hierarchy and programmability.
Figure 1: The OpenFog Reference Architecture(https://www.openfogconsortium.org/ra/)
A detailed architecture stack shows the interrelationships between various hardware, software
infrastructure, and application software layers, as well as various cross-cutting concernssuchas

37
security, performance, manageability, analytics and controlthat impact the function of all
layers. As security is one of the most complex and critical aspects of IoT systems, a special
appendix dives deeply into OpenFog security guidelines. The OpenFog architecture is depicted in
figure 1 above.
2. RELATED WORK
Although there are numerous novel studies contributed tothe secure authentication system, recent
studies have mostly considered cloud storage environments rather than fog computing. In this
section, we briefly summarize some few ones among those that address fog computing and point
out their limitations and difficulties in direct adoption to the fog storage architecture.
Some authentication protocols which have been proposed for fog computing systems were
described in many articles. However, only a few of them can achieve privacy preservation. The
first type of such authentication protocols uses symmetric key encryption algorithmsdue to their
low computational cost [11][21][22]. However, these protocols can be attacked by man-in-the-
middle attacks, and the privacy information will inevitably be revealed. Another drawback of
these protocols is the inherent scalability problem for privacy preservation, which makes them
undoubtedly impractical. The second type of such authentication protocols updates an end-
device’s credentials regularly[13][24][25]. However, during the validity period of the credential,
the strong identity of an end-device can still be tracked. Furthermore, such protocols require each
end-device to store a large number of certifications and pseudonyms, which means that it is
difficult to remove a compromised end-device. The third type of such authentication protocols
uses a delegation-based mechanism [5]. The advantage is their low computational cost, but the
disadvantage is that the privacy-preserving property cannot be easily achieved. To overcome this
limitation, we proposed a three-factor authentication using both smart card and biometrics.
3. SECURITY CHALLENGES
In spite of the fact that Fog Computing can play a central role in delivering a rich portfolio of
services more effectively and efficiently to end users, it could impose security and privacy
challenges. The major security and privacy challenges in fog computing are summarized below.
TRUST MODEL.
Trust models based on reputation have been successfully deployed in many scenarios such as
online social networks. Reputation-based trust model proposed by [10]has been successful in
eCommerce, peer-to-peer (P2P), user reviews and online social networks.
Research conducted by[7], proposed a robust reputation system for resource selection in P2P
networks using a distributed polling algorithm to assess the reliability of a resource before
downloading. In designing a fog computing reputation-based reputation system, we may need to
tackle issues such as
a) How to achieve persistent, unique, and distinct identity,
b) How to treat intentional and accidental misbehavior,
c) How to conduct punishment and redemption of reputation.

38
There are also trusting models based on special hardware such as Secure Element (SE), Trusted
Execution Environment (TEE), or Trusted Platform Module (TPM), which can provide trust
utility in fog computing applications[17].
Research conducted by[18], it wassuggestedthat to design a trust model based on reputation in the
IoT, we need to tackle how to maintain the service reliability and prevent accidental failures,
handle and identify misbehavior issues, identify malicious behavior correctly, and bootstrap
building a trust model based on reputation in large-scale networks.
ROGUE FOG NODE
A rogue fog node would be a fog device or fog instance that pretends to be legitimate and coaxes
end users to connect to it. For example, in an insider attack, a fog administrator may be
authorized to manage fog instances, but may instantiate a rogue fog instance rather than a
legitimate one. [20] have demonstrated the feasibility of man-in-the-middle attack in fog
computing, before which the gateway should be either compromised or replaced by a fake one.
Once connected, the adversary can manipulate the incoming and outgoing requests from end
users or fog, collect or tamper user data stealthily, and easily launch further attacks.The existence
of fake fog node will be a big threat to user data security and privacy. This problem is hard to
address in fog computing due to several reasons
a) Complex trust situation calls for different trust management schemes,
b) Dynamic creating, deleting of virtual machine instance make it hard to maintain a
blacklist of rogue nodes.
A rogue IoT node has the potential to misuse users' data or provides malicious data to
neighboring nodes to disrupt their behaviors. Addressing this problem could be difficult in the
IoT due to the complexity in trust management in various schemes. However, a trust
measurement-based model could be applied to detect rogue nodes in IoT environments' which can
provide limited security protection.
AUTHENTICATION
Authentication is an important issue for the security of fog computing since services are offered
to massive-scale end users by front fog nodes. [20] have considered the main security issue of fog
computing as the authentication at different levels of fog nodes. Traditional PKI-based
authentication is not efficient and has poor scalability. [2] have proposed a cheap, secure and
user-friendly solution to the authentication problem in local ad-hoc wireless network, relying on a
physical contact for pre-authentication in a location-limited channel.
As the emergence of biometric authentication in mobile computing and fog computing, such as
fingerprint authentication, face authentication, touch-based or keystroke-based authentication,
etc., it will be beneficial to apply biometric-based authentication in fog computing.

39
ACCESS CONTROL
As per [1], Access control is a security technique to ensure that only authorized entities can
access a certain resource, such as an IoT device, or the collected data. In the IoT, we need access
control to make sure that only trusted parties can perform a given action such as accessing IoT
device data, issuing a command to an IoT device, or updating IoT device software.
Research conducted by[9],propose a policy-based resource access control in fog computing, to
support secure collaboration and interoperability between heterogeneous resources. In fog
computing, how to design access control spanning client-fog-fog, at the same time meet the
designing goals and resource constraints will be challenging.
INTRUSION DETECTION
As per [15],Intrusion detection techniques are widely deployed in fog system to mitigate attacks
such as insider attack, flooding attack, port scanning, attacks on VM and hypervisor. In fog
computing, Intrusion Detection System (IDS) can be deployed on fog node system side to detect
intrusive behavior by monitoring and analyzing log file, access control policies and user login
information. They can also be deployed at the fog network side to detect malicious attacks such
as denial-of-service (DoS), port scanning, etc. In fog computing, it provides new opportunities to
investigate how fog computing can help with intrusion detection on both client-side and the
centralized fog side.
Research conducted by[19], a foglet mesh based security framework which can detection
intrusion to distance fog, securing communication among mobile devices, foglet and fog. There
are also challenges such as implementing intrusion detection in geo-distributed, large-scale, high-
mobility fog computing environ men to meet the low-latency requirement.
4. BIOMETRIC SMARTCARD AUTHENTICATION FOR FOG COMPUTING
SERVICES – A PROTOTYPE
Biometric identification is utilized to verify a person’s identity by measuring digitally certain
human characteristics and comparing those measurements with those that have been stored in a
template for that same person. Details can be found in [10]. Templates can be stored at the
biometric device, the institution’s database, a user’s smart card, or a Trusted Third Party service
provider’s database. There are two major categories of biometric techniques: physiological
(fingerprint verification, iris analysis, hand geometry-vein patterns, ear recognition, odor
detection, DNA pattern analysis and sweat pores analysis), and behavioral (handwritten signature
verification, keystroke analysis and speech analysis) [16].Research conducted by[8], it was found
that behavior-based systems were perceived as less acceptable than those based on physiological
characteristics. Of the physiological techniques, the most commonly utilized is that of fingerprint
scanning. With biometrics, fraudulent incidents can be minimized, as an added layer of
authentication is now introduced that ensures that even with the correct pin information and in
possession of another person's card, the user’s biometric features cannot easily be faked. The
advantages of this may include: all attributes of the cards will be maintained, counterfeiting
attempts are reduced due to enrolment process that verifies identity and captures biometrics, and
it will be extremely high security and excellent user-to-card authentication. These advantages are

40
for the benefit of users as well as system administrators because the problems and costs
associated with lost, reissued or temporarily issued can be avoided, thus saving some costs of the
system management.
On the negative side, the major risk posed by the use of biometric systems is that a malicious
subject may interfere with the communication and intercept the biometric template and use it later
to obtain access [14]. Likewise, an attack may be committed by generating a template from a
fingerprint obtained from some surface. Although few biometric systems are fast and accurate in
terms of low false acceptance rate enough to allow identification (automatically recognizing the
user identity), most of the current systems are suitable for the verification only, as the false
acceptance rate is too high.
The proposed design uses a maximum of 8 characters, numbers or mix of both PIN and
fingerprint as verification factors of the authentication process. ACOS smartcards and AET60
BioCARDKey development kit were used in the proposed design. In the verification part, the
users have to submit the correct PIN DES encrypted current session key to get access to the next
level. Users have 3 successful attempts to enter the correct PIN, else the cards will be locked and
render it to useless. Lastly, Authors use the fingerprint as the biometric identifiers as it takes
shortest enrollment time. The proposed design involves two phases namely the enrollment phase
and verification phase. Each of the phases is briefly described below.
Enrollment - Prior to an individual being identified or verified by a biometric device, the
enrollment process must be completed. The objective of this enrollment process is to create a
profile of the user. The process consists of the following two steps. The screenshots of the
prototype are shown in figure also shown in figure 3:
1. Sample Capture: the user allows for a minimum of two or three biometric readings, for
example: placing a finger in a fingerprint reader. The quality of the samples, together
with the number of samples taken, will influence the level of accuracy at the time of
validation. Not all samples are stored; the technology analyzes and measures various data
points unique to each individual. The number of measured data points varies in
accordance to the type of device.
2. Conversion and Encryption: the individual’s measurements and data points are converted
to a mathematical algorithm and encrypted. These algorithms are extremely complex and
cannot be reversed engineered to obtain the original image. The algorithm may then be
stored as a user’s template in a number of places including servers and card.
A new and blank card has to be enrolled with user details before it can be verified later.
Enrollment system is usually operated by the admin to enter their user's details into the card.
However, the exception applies to the PIN entry where it should be entered by the user
themselves and need to enter the PIN again to make sure they enter the correct ones.

41
Figure2: Flowchart for the Enrollment Process
Figure3: Enrollment Process
Identification and Verification - Once the individual has been enrolled in a system, he/she
can start to use biometric technology to have access the enrolled services from the fog database
server. The screenshots of the prototype are shown in the figure also shown in figure 5:
1. Identification: a one-to-many match. The user provides a biometric sample and the
system looks at all user templates in the database. If there is a match, the user is granted
access, otherwise, it is declined.
2. Verification: a one-to-one match requiring the user provides identification such as a PIN
and valid card in addition to the biometric sample. In other words, the user is establishing
who he/she is and the system simply verifies if this is correct. The biometric sample with
the provided identification is compared to the previously stored information in the
database. If there is a match, access is provided, otherwise, it is declined.

42
Figure4: Flowchart for the Verification Process
Figure5: Verification Process
After the card has been enrolled with user data, this particular card will be the user’s ID. The PIN
and fingerprint sample from the user wasalso encrypted and save into the card. In order to get
access the fog server, the user has to present the card to the card reader, and then verify the PIN
and lastly matched their fingerprint detail with the card. The sequence diagram in Figure 6 below
summarizes the verification process.

43
Figure 6: Process for granting access to service
5. CONCLUSION
In this paper, authors have explained the concepts of fog computing, and authors have presented
the benefits, properties, and characteristics of fog computing and security issue related to it. On
the other hand, authors have explained how to achieve the authentication of fog computing using
the three-factor authentication method. Authors designed the new efficient model based on
fingerprint, the implemented model works on storage all the user's fingerprints with their
password on fog server
6. FUTURE STUDY
Biometrics increasingly form the basis of identification and recognition across many sensitive
applications. But as the use of biometric systems increases, so do the threats against them. The
secure storage of biometric templates has therefore, become a key issue in the modern era; the
acceptance of biometric authentication devices by the general public is dependent on the
perceived level of security of biometric information templates stored within databases. Privacy
concerns have grown because a biometric template is a unique identifier of a person. And while
the template cannot be decoded back to the biometric data, it may be used to track the individual.
If there is a database that ties the user to their unique biometric template, it could be used illegally
to monitor the activities of the user. Such threats need to be addressed, and one potential solution
is cancellable biometrics. This is a template transformation technique that uses intentional
repeated distortions to provide security to biometric templates; the distortions can be performed
either at the signal level or at the feature level to achieve a transformed template. It is therefore
important for further studies on cancellable biometrics and its application in IoT
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Author
Kashif Munir received his BSc degree in Mathematics and Physics from Islamia
University Bahawalpur, Pakistan in 1999. He received his MSc degree in
Information Technology from University Sains Malaysia in 2001. He also obtained
another MS degree in Software Engineerin
2005. He completed his PhD in Informatics from Malaysia University of Science
and Technology, Malaysia. His research interests are in the areas of Cloud
Computing Security, Software Engineering, and Project Manageme
published journal, conference papers and book chapters.
Kashif Munir has been in the field of higher education since 2002. After an initial teaching experience with
courses in Stamford College, Malaysia for around four years, he later relocated
with King Fahd University of Petroleum and Minerals, KSA from September 2006 till December 2014. He
moved into University of Hafr Al-Batin, KSA in January 2015.
Kashif Munir is a researcher and published author/editor of
such as Security in Cloud Computing, Mobile Cloud and Green Enterprises,
(https://www.amazon.com/Kashif-Munir/e/B079KP1LFJ
Lawan A. Mohammad, Holds a PhD degree in computer and communication systems engineering from
University Putra Malaysia. Research interest include smartcard security, authentication protocols, wireless
and mobile security, biometrics, mathematical programming and e
Shi, Y., Abhilash, S., & Hwang, K.(2015). Cloudlet mesh for securing mobile fogs from intrusions
and network attacks. In: 3rd IEEE International Conference on Mobile Cloud Computing, Services,
Stojmenovic, I., & Wen, S.(2014). The fog computing paradigm: scenarios and security issues. In:
Proc. of the 2014 Federated Conference on Computer Science and Information Systems (FedCSIS)
Tsai, H., Chang, C., & Chan, K. (2009). Roaming across wireless local area networks using SIM
based authentication protocol, Computer Standard Interfaces 31 (2) pp.381–389.
SIM-based subscriber authentication mechanism for wireless local area
networks, Computer Communications. 29 (10) pp. 1744–1753.
Merino, L. (2014). Finding your way in the fog: towards a comprehensive
g. ACM SIGCOMM CCR44(5), 27–32
Zhu, H., Lin, X., Lu, R., Ho, P., & Shen, X. (2008). AEMA: An aggregated emergency message
iello, G., Papadimitratos, P., Hubaux, J-P., &Lioy, A. (2007). , Efficient and robust
pseudonymous authentication in VANET, in: Proc. VANET, pp. 19–28.
https://www.howtogeek.com/185876/what-is-fog-computing/
received his BSc degree in Mathematics and Physics from Islamia
University Bahawalpur, Pakistan in 1999. He received his MSc degree in
Information Technology from University Sains Malaysia in 2001. He also obtained
another MS degree in Software Engineering from University of Malaya, Malaysia in
2005. He completed his PhD in Informatics from Malaysia University of Science
and Technology, Malaysia. His research interests are in the areas of Cloud
Computing Security, Software Engineering, and Project Management. He has
published journal, conference papers and book chapters.
courses in Stamford College, Malaysia for around four years, he later relocated to Saudi Arabia. He worked
Batin, KSA in January 2015.
Kashif Munir is a researcher and published author/editor of 4 books on cloud computing including subjects
Munir/e/B079KP1LFJ).
PhD degree in computer and communication systems engineering from
and mobile security, biometrics, mathematical programming and e-learning.
45
Shi, Y., Abhilash, S., & Hwang, K.(2015). Cloudlet mesh for securing mobile fogs from intrusions
Conference on Mobile Cloud Computing, Services,
Stojmenovic, I., & Wen, S.(2014). The fog computing paradigm: scenarios and security issues. In:
Systems (FedCSIS)
Tsai, H., Chang, C., & Chan, K. (2009). Roaming across wireless local area networks using SIM-
based subscriber authentication mechanism for wireless local area
Merino, L. (2014). Finding your way in the fog: towards a comprehensive
Zhu, H., Lin, X., Lu, R., Ho, P., & Shen, X. (2008). AEMA: An aggregated emergency message
P., &Lioy, A. (2007). , Efficient and robust
to Saudi Arabia. He worked
on cloud computing including subjects
PhD degree in computer and communication systems engineering from

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