A017360104

IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. VI (May – Jun. 2015), PP 01-04
www.iosrjournals.org
DOI: 10.9790/0661-17360104 www.iosrjournals.org 1 | Page
Comparative Study on Intrusion Detection Systems for
Smartphones
Supriya Kamble 1
, Leena Ragha 2
, Puja Padiya3
1,2,3
(Department of Computer Engineering, Ramrao Adik Institute of Technology, Navi Mumbai, India)
Abstract: Now-a-days the usage of Smartphone has been increasing greatly in recent years. Most of the people
are de-pendent on Smartphone for all sort of activities such as checking mails, browsing internet, performing
online transactions, surfing social networks, shopping online, paying bills etc. With so many advantages in
Smartphone for users, the threats to user are also increasing. The threats are caused by creating malicious
applications and game of which most of them are freely available to users on Google play. As the Smartphone
have limited processing and computational power to execute highly complex algorithms for intrusion detection,
virtual Smartphone images are created in cloud to prevent user from threats and vulnerabilities. In this paper
we perform a comparative study on existing methods on intrusion detection system on cloud and on host devices
for securing Smartphone. Cloud intrusion detection system is a better solution to achieve higher level of
security. The paper discusses architectures of existing Intrusion detection system for Smartphone and their
techniques
Keywords: Intrusion Detection System, Cloud Computing, Smartphones, Android Security.
I. Introduction
Smartphones usage have been continuously growing in recent times with the advent of internet.
Smartphones offer Personal Computer functionality to the end user and are vulnerable to the same sorts of
security threats. Smartphone are extremely fast growing type of communication devices offering more advanced
computing and connectivity functionalities than contemporary mobile phones [2]. With rapidly growing
popularity more and more people and companies are using these devices making it more integrated and
prevalent part of people daily lives [3].
People use their smartphone to keep their data, to browse the internet, to exchange messages, to check
emails, to play games, to keep notes, online shopping, online banking, bill paying, to carry their personal files
and documents, etc. Various models of smartphones have been released catering to the various demands of
mobile users. A smartphone user needs to install and run third-party software applications. There are, lot of third
party applications available in free of cost on Google Play and various other store website. Its easy availability
encourages attackers to build malicious applications for such devices [1]. Being all-in-one device, the
smartphones are increasingly getting attractive to a wide range of users [2]. With the advent of internet, the
mobile network infrastructure quality and affordability consistently improved, thus usage of smart mobile
phones for financial transactions, mobile learning and web browsing is becoming popular among users which
causes several security issues [1].
With such an increasing popularity of the smartphones attacks threats are also increasing. Also as the
device is coupled with the always on connectivity to the Internet that wireless networks allow, mobile
technology is potentially vulnerable to increasing number of malicious threats Smartphones are more vulnerable
to malware attacks, Trojans and viruses [10].
Distribution of applications is made easy for the developer by offering a central distribution market,
where every developer can upload own applications, and the user simply downloads it in very few steps.
Malicious application scan also get distributed in this manner, because only little security scanning, whether an
application behaves malicious or benign, is applied. These facts show, that there is a high demand for solutions
which increase the security of the devices. One approach to mitigate the limited capabilities of smartphones (e.g.
processing power and battery capacity), is to off-load workload into the cloud. Taking advantage of the cloud is
a very promising approach, since a service in the cloud can be modified as needed, whereas modifications to the
smartphones are more difficult.
The rest of the paper is organized as follows. Section II presents the related work. Section III presents
existing IDS framework for Smartphone. Section IV gives the detailed comparison and analysis of different IDS
methods described in Section III by considering different parameters. Finally, Section V concludes the paper.

Comparative Study on Intrusion Detection Systems for Smartphones…
II. Literature Review
Khune and Thangakumar [1] proposed a cloud based intrusion detection and recovery system for
Android smartphones. The framework performs in-depth forensics analysis and detect any malicious activity in
network. The users of smartphone gets register to cloud-based services specifying relevant in-formation about
the operating system, device, applications. A light-weight mobile agent on the user’s smartphone. In the cloud
environment intrusion detection and in-depth analysis is performed. The result of detail analysis and recovery
methods are sent to the mobile host on the device to take necessary actions. An optimal protection and recovery
is provided by the framework.
Halilovic et. al. [4] has proposed and developed a conceptual AmoxID model for android devices. The
proposed model is generally useful for companies who needs to protect their company data. The proposed model
enforces certain policy levels depending upon employees network locations i.e. Office Network, Home Network
or Outdoor Networks. The employees smartphone is configured with pre-built IDS enforcing policies protecting
access to company data on the phone. The model uses SVM classifications enforcing policies based on type of
network the user is connect to categorizing threats on the devices.
Ghorbanian et. al. [5] proposed a host-based intrusion detection model. The model analyzes security of
smartphone for android devices providing an active defense system for android security user. The application is
developed in the area of smartphone security and analyzes the log file generating a response for intrusion. The
proposed system detects attacks using pattern matching algorithm.
Shabtai A and Elovici Y [6] has proposed a light-weight, behavioral-based detection framework called
Andromaly for Android smartphones based on Host-based Intrusion Detection System (HIDS). The detection
system runs directly on the device, monitoring various features and events on the smartphone and classifies
them as benign or malicious. Several combinations of classification algorithms and feature selections for
evaluation and conclude that the proposed anomaly detection is feasible on Android devices.
Jacob [7] proposed cloud based intrusion detection and response engine, which performs an in-depth
forensics analysis. An intrusion is detected using cloud service and if any corrupted file or misbehavior is
detected, corresponding response actions are taken by the system to handle the threat. The system produces
accurate intrusion detection and response.
III. Existing Ids For Smartphone
A. Security as a Service Based Anomaly IDS
In the paper [1] the author had proposed a cloud based IDS and recovery system for android. The
proposed architecture uses the cloud services i.e., platform as a service and security as a service for performing
intrusion detection. A lightweight mobile host is installed on the mobile device which inspects the file activity
on the system. Firstly, the target device is registered on the cloud server application. The cloud server
application deploys security methods such as emulator, memory scanners, system call anomaly detection and
antivirus software. The mobile host generates a unique identifier of the file, which is compared against a cache
of previous analyzed files and is sent to the in-cloud network analysis if the file is not present. After the analysis
of file, the results are stored in both local cache on the mobile host agent and a shared remote cache in the cloud
computing services. The proxy server acts as a mediator which mirrors the ongoing traffic between the mobile
device and internet and sends it to cloud services for further analysis. It controls the access of devices to various
applications and services.
B. Signature-Based HIDS
In [5] proposed system, the user has to authenticate to the system by creating an account. The log files
from the device are fed to the system. The Log File Decoder Module changes the record into a defined format
for system analysis and the result is send to the Detection Engine which compares the records with the rule-sets.
In case of no matching item, natural action is done and the system goes to this next record to process. With the
purpose of adapting the changing Internet and new intrusion behavior, the proposed system has Update Rule-set
interface to update rule-set which is enable to detect.
C. AMOXID IDS
In [4] the author proposes a host based IDS named AmoxID for smartphones with a proof of concept.
The model proposes categorization of threats into three main categories: 1-Threats to user’s experience; 2-Cost
generating threats; 3-Privacy in-fringing threats. Each category is analyzed separately and deals with three
different subsystems in IDS for smartphones.
The model proposes system of policies depending on the user’s current network, different policy levels
is applied. To create the proof of concept the model is used in a company where employees are provided with a
smartphone which require them to follow certain policy. If company sends confidential emails and give
confidential data to employees that are accessed through smartphone, then it is important to protect this

information. Special designing policies are included in pre-built IDS enforcing various policies depending on
the users current network. The features such as numbers of outgoing call, outgoing SMS, connection to GPRS
are tracked using SVM classification.
D. Andromaly Framework
The paper [6] proposes a andromaly behavioral-based detection framework which realizes on HIDS
monitoring various features and events from the device. Machine learning methods are applied to classify the
collected data as normal or ab-normal. The framework evaluates games and tool applications effectively
detecting application having similar behavior. The feature extractor collects various features from the device and
pre-process the raw features. The processor performs analysis and generate output threats assessment which are
given to the threat weighting unit. The threat weighting unit applies ensemble algorithms (such as Majority
Voting, Distribution Summation etc.) to derive a final coherent decision regarding the infection level in device.
The service agent is an important component which synchronizes feature collection, alert process and malware
detection. The graphical user interface configures the agent’s parameters, activate or deactivate, visual
exploration and visual alerting of collected data.
E. Anomaly Based IDS
The paper [7] proposed a proactive defense mechanism in which the smartphone user is given the alert
before downloading the file. The author created a web server where contents are entered. The properties of all
the files are entered into a cloud server and also a string matching algorithm is entered into the cloud for
comparison. The user first registers itself specifying the device OS and application lists, so an emulated image is
created in cloud. The communication between the smartphone and the Internet is duplicated and forwarded to
the emulator in cloud where the detection, forensics analyses are performed. The monitoring and detecting
process is developed in cloud for identifying any intrusion in the web server. When the request is send by the
client it is forwarded to the cloud where cloud server identifies any change in the contents of the file based on
the string matching algorithm. If any unsecured file or misbehavior is detected, system takes the corresponding
response actions to handle the threat. This system produces accurate intrusion detection and is scalable to any
number of users.
IV. Table 1: Comparison & Analysis
V. Conclusion
With the growing use of Smartphone, the number of attacks and threats are also on increase. It is
necessary to provide security to end users from threats. In above section we have studied various existing IDS
for smartphone each based on single type of IDS (Anomaly based IDS or Signature based IDS) which restricts
the detection of attacks.
Papers
Parameters
Cloud-Based IDS for
Android
Smartphone
Signature –Based
Hybrid IDS for
Android
Intrusion
Detection on
Smartphone
Applying
Behavioral
Detection on
Android Device
Intrusion Detection on
Cloud for Smartphone
Method Anomaly Based Signature Based Rule Based Anomaly based Signature Based
Type of Detection NIDS HIDS HIDS HIDS NIDS
Positioning At Cloud On Host On Host On Host At Cloud
Service Used SeaaS - - - SaaS
Analysis Performs in –depth
analysis and provides
recovery
Active defense
mechanism.
Low false positive
and negative
Provides optimal
protection against
threat
High true positive
rate
Alerts for abnormal
behavior
Scalable Yes No Yes No Yes
Pros -Provides optimal
protection.
-Parallel multiple
detection engines
provides good
detection of attack
-Higher detection rate
and accuracy
-Update rule interface
allows to detect
modified attacks
-Analyzes threats at
3 levels i.e., threats
to user experience,
threat to generate
cost, privacy
infringement threats
-Provides optimal
protection
-Lower false alarm
rate
-Proactive defense
mechanism
-Performs optimal
response actions against
abnormal behavior
Cons -More false alarms as
user and network
behavior are not
known beforehand
-Rule set needs to de
updated
-Requires different
policy rules for
different levels of
alert
-Requires large
matching data set
-Detection accuracy
based on amount of
calculated behavior
or features
-Requires large data sets
for accurate calculations

The main characteristic of signature based IDS is detection of incoming threats against a predefined
knowledge base whereas in anomaly based IDS detects unexpected change in the system behavior from a
normal behavior. In future, the combination of both anomaly and signature based IDS, the performance of attack
detection can be increased thus preventing the smartphone from any malicious attack.
References
[1]. Rohit S. Thune, J. Thangakumar,“A Cloud-Based Intrusion Detection System for Android Smartphones,”
[2]. Radar, Communication and Computing (ICRCC), 2012 International Conference on, vol., no., pp.180-184, 21-22 Dec. 2012.
[3]. Amir Houmansadr, Saman A. Zonouz, and Robin Berthier,“A Cloud-based Intrusion Detection and Response System for Mobile
Phones,” Dependable Systems and Networks Workshops (DSN-W), 2011 IEEE/IFIP 41st International Conference on, vol., no.,
pp.31-32, 27-30 June 2011.
[4]. Dr.Marwan Omar, Dr. Maurice Dawson,“Reseach in Progress-Defending Android Smartphones from Malware Attacks,” Advanced
Computing and Communication Technologies (ACCT), 2013 Third International Conference on, vol., no., pp.288-292, 6-7 April
2013.
[5]. Muhamed Halilovic, Abdulhamit Subasi, “Intrusion Detection on Smartphone”.
[6]. Masoud Ghorbanian, Bharanidharan Shanmugam, Ganthan Narayansamy, Norbik Bashah Idris,“Signature-Based Hybrid Intrusion
Detection System(HIDS) for Android Devices,” Business Engineering and Industrial Applications Colloquium (BEIAC), 2013
IEEE, vol., no., pp.827-831, 7-9 April 2013.
[7]. Asaf Shabtai, Yuval Elovici,“Applying Behavioral Detection on Android-Based Devices,” Mobile Wireless Middleware, Operating
Sys-tems, and Applications, Springer, vol.48, no., pp.235-249, 2010.
[8]. Namita Jacob,“Intrusion Detection In Cloud for Smart Phones,” IJREAT International Journal of Research in Engineering &
Advanced Technology on, vol.1, no.1, pp., March 2013.
[9]. Han Bing,“Analysis and Research of System Security Based on An-droid.” Intelligent Computation Technology and Automation
(ICICTA), 2012 Fifth International Conference on, vol., no., pp.581-584, 12-14 Jan. 2012.
[10]. McAfee Threat Report: Second Quarter 2013: http://www.mcafee.com/ca/resources/reports/rp-quarterly-threat-q2-2013.pdf
[11]. Jazilah Jamaluddin, Nikoletta Zotou, Reuben Edwads, Paul Coulton,“Mobile Phone Vulnerabilities: A New Generation of
Malware,” Consumer Electronics, 2004 IEEE International Symposium on, vol., no., pp.199-202, 1-3 Sept. 2004.
[12]. National Institute of Standards and Technology. The NIST definition of cloud computing:
http://csrc.nist.gov/publications/nistpubs/800-145/SP800-145.pdf, (retrieved at2012-05-10).
[13]. Jon Oberheide, Kaushik Veeraraghavan, Evan Cooke, Jason Flinn, Farnam Jahanian,“Virtualized In-Cloud Security Services For
Mobile Devices,” MobiVirt ’08 Proceedings of the First Workshop on Virtualization in Mobile Computing on, vol., no., pp.31-35,
2008.
[14]. Hatem Hamed, Mahmoud Al-Hoby,“Managing Intrusion Detection as a Service in Cloud Networks,” International Journal of
Computer Applications on, vol.41 no.1, pp.35-40, March 2012.
[15]. Asaf Shabtai,“Malware Detection on Mobile Devices,” Mobile Data Mangament (MDM), 2010 Eleventh International Conference
on, vol., no., pp.289-290, 23-26 May 2010.

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