Research Article | OPEN ACCESS
Enhanced Permission Based Malware Detection in Mobile Devices Using Optimized Random Forest Classifier with PSO-GA
M. Sujithra and G. Padmavathi
Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, India
Research Journal of Applied Sciences, Engineering and Technology 2016 7:732-741
Received: October 5, 201 | Accepted: November 4, 2015 | Published: April 05, 2016
Abstract
Smartphones and mobile devices are rapidly growing with their popularity as a part of global infrastructure powered communication system. As mobile devices become ubiquitous, used for a wide variety of application areas like personal communication, data storage, accessing online information, making payment, etc. The tremendous growth of smartphone usage makes it a target for malicious attackers to propagate malware attacks. Increased demand for mobile devices is due to the huge availability of applications that can be downloaded and installed easily on these devices. It is difficult for the general users to differentiate between the set of permissions which are potentially harmful and those which are not. This paper proposes to solve these issues by enhanced machine learning based malware detection framework using optimization algorithms. New classifier is developed by integrating GA and PSO with Random Forest algorithm. The Outcome from this paper is a new MSGP Malware Detection System consisting of MSGP-MDS Classifier. This reveals that classification of Android APK files using PSO plays a critical role in realizing higher accuracy with the minimum computation resource requirement.
Keywords:
Android, goodware, machine learning, malware, malware detection, optimization technique,
References
-
Abela, L.K.J., E.D.K. Angeles, P.D.A.J. Raynier, R.J. Tolentino and N.A.G. Miguel, 2013. An automated malware detection system for android using behavior-based analysis AMDA. Int. J. Cyber-Secur. Digit. Foren., 2(2): 1-11.
-
Ahandani, M.A. and M.T.V. Baghmisheh, 2013. Hybridizing genetic algorithms and particle swarm optimization transplanted into a hyper-heuristic system for solving university course timetabling problem. WSEAS T. Comput., 12(3): 128-143.
-
Aung, Z. and W. Zaw, 2013. Permission-based android malware detection. Int. J. Sci. Technol. Res., 2(3): 228-234.
-
Bahrololum, M., E. Salahi and M. Khaleghi, 2009. Machine learning techniques for feature reduction in intrusion detection systems: A comparison. Proceeding of the 4th International Conference on Computer Sciences and Convergence Information Technology (ICCIT'09). Seoul, pp: 1091-1095.
CrossRef -
Damopoulos, D., S.A. Menesidou, G. Kambourakis, M. Papadaki, N. Clarke and S. Gritzalis, 2011. Evaluation of anomaly-based IDS for mobile devices using machine learning classifiers. Secur. Commun. Netw., 5(1): 3-14.
CrossRef
-
Denison, D.G.T., B.K. Mallick and A.F.M. Smith, 1998. A Bayesian CART algorithm. Biometrika, 85(2): 363-377.
CrossRef
-
ESET Labs, 2013. Trends for 2013: Astounding Growth of Mobile Malware. Retrieved from: http://go.eset.com/us/resources/white-papers/Trends_for_2013_ preview.pdf.
Direct Link
-
Fedler, R., J. Schutte and M. Kulicke, 2013. On the effectiveness of malware protection on android: An evaluation of android antivirus apps. Technical Report Antivirus Test, Fraunhofer Research Institution for Applied and Integrated Security, Fraunhofer AISEC, pp: 1-35.
-
Garcia, J.S.D., S.L. Ávila and W.P. Carpes Junior, 2006. Introduction to optimization methods: A brief survey of methods. IEEE Multidiscipl. Eng. Educ. Mag., 1(2): 1-5.
-
Glodek, W. and R. Harang, 2013. Rapid permissions-based detection and analysis of mobile malware using random decision forests. Proceeding of the IEEE Military Communications Conference (MILCOM, 2013). San Diego, CA, pp: 980-985.
CrossRef
LI id="Ref11">
Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I.H. Witten, 2013. The WEKA data mining software: An update. ACM SIGKDD Explor. Newslet., 11(1): 10-18. CrossRef
-
Ham, H.S. and M.J. Choi, 2013. Analysis of Android malware detection performance using machine learning classifiers. Proceeding of the International Conference on ICT Convergence (ICTC, 2013). Jeju, pp: 490-495.
-
Hein, C.L.P.M., 2014. Permission based malware protection model for android application. Proceeding of the International Conference on Advances in Engineering and Technology (ICAET'2014). Singapore, pp: 222-226.
-
Kumar, A. and S. Kumar, 2014. Decision tree based learning approach for identification of operating system processes. WSEAS T. Comput., 13: 277-288.
-
Rastogi, V., Y. Chen and X. Jiang, 2013. Droidchameleon: Evaluating android anti-malware against transformation attacks. Proceeding of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security (ASIA CCS '13), pp: 1-6.
-
Sanz, B., I. Santos, C. Laorden, X. Ugarte-Pedrero, P.G. Bringas and G. Álvarez, 2013. PUMA: Permission usage to detect malware in android. Proceeding of the International Joint Conference on CISIS'12-ICEUTE´ 12-SOCO´ 12 Special Sessions. Springer, Berlin, Heidelberg, 189: 289-298.
-
Senthilkumar, S. and T. Kannan, 2014. Multi-objective optimization of bead geometry and dilution in FCAW process using PSO. Int. J. Appl. Eng. Res., 9(24): 25817-25832.
-
Silva, L.O.L.A., M.L. Koga, C.E. Cugnasca and A.H.R. Costa, 2013. Comparative assessment of feature selection and classification techniques for visual inspection of pot plant seedling. Comput. Electron. Agr., 97: 47-55.
CrossRef
-
Sujithra, M. and G. Padmavathi, 2012. Mobile device security: A survey on mobile device threats, vulnerabilities and their defensive mechanism. Int. J. Comput. Appl., 56(14): 24-29.
-
Wei, X., L. Gomez, I. Neamtiu and M. Faloutsos, 2012. Permission evolution in the android ecosystem. Proceeding of the 28th Annual Computer Security Applications Conference (ACSAC'12). NY, USA, pp: 31-40.
CrossRef
-
Xie, L., X. Zhang, J.P. Seifert and S. Zhu, 2010. pBMDS: A behavior-based malware detection system for cellphone devices. Proceeding of the 3rd ACM Conference on Wireless Network Security, pp: 37-48.
CrossRef
-
Yusoff, M.N. and A. Jantan, 2011. A framework for optimizing malware classification by using genetic algorithm. In: Zain, J.M. et al. (Eds.), ICSECS, 2011. Part II, Communications in Computer and Information Science, Springer-Verlag, Berlin, Heidelberg, 180: 58-72.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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The authors have no competing interests.
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