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Publisher

The Association of Digital Forensics, Security and Law (ADFSL)

Abstract

This paper analyzes different Android malware detection techniques from several research papers, some of these techniques are novel while others bring a new perspective to the research work done in the past. The techniques are of various kinds ranging from detection using host based frameworks and static analysis of executable to feature extraction and behavioral patterns. Each paper is reviewed extensively and the core features of each technique are highlighted and contrasted with the others. The challenges faced during the development of such techniques are also discussed along with the future prospects for Android malware detection. The findings of the review have been well documented in this paper to aid those making an effort to research in the area of Android malware detection by understanding the current scenario and developments that have happened in the field thus far.

References

Apvrille, A., & Strazzere, T. (2012). Reducing the window of opportunity for Android malware gotta catch ’em all. Journal in Computer Virology, 8(1-2): 61-71.

Burguera, I., Zurutuza, U., & Nadjm-Tehrani, S. (2011). Crowdroid: Behaviorbased malware detection system for Android. 2011 ACM CCS Workshops on Security and Privacy in Smartphones and Mobile Devices (SPSM’11), 17-21 October 2011, Chicago, Illinois, USA.

Daryabar, F., Dehghantanha, A., & Broujerdi, H. G. (2012). Investigation of malware defense and detection techniques. International Journal of Digital Information and Wireless Communications (IJDIWC), 1(3): 645-650.

Daryabar, F., Dehghantanha, A., & Udzir, N. (2011). Investigation of bypassing malware defenses and malware detections. 7th International Conference on Information Assurance and Security (IAS), 5-8 December 2011, Malacca, Malaysia.

Grace, M., Zhou, Y., Zhang, Q., Zou, S., & Jiang, X. (2012). RiskRanker: scalable and accurate zero-day Android malware detection. The 10th International Conference on Mobile Systems, Applications, and Services (MobiSys’12), Low Wood Bay, Lake District, United Kingdom.

Isohara, T., Takemori, K., & Kubota, A. (2011). Kernel-based behavior analysis for Android malware detection. 2011 Seventh International Conference on Computational Intelligence and Security, 3-4 December 2011, Sanya, Hainan Province, China.

Mohtasebi, S. H., & Dehghantanha, A. (2011). A mitigation approach to the privacy and malware threats of social network services. Digital Information Processing and Communications, Springer Berlin Heidelberg.

Sahs, J., & Khan, L. (2012). A machine learning approach to Android malware detection. 2012 European Intelligence and Security Informatics Conference, 22-24 August 2012, Odense, Denmark.

Schmidt, A., Bye, R., Schmidt, H., Clausen, J., Kiraz, O., Yuksel, K., … Albayrak, S. (2009). Static analysis of executables for collaborative malware detection on Android. IEEE International Conference on Communications Workshops (IEEE ICC 2009), 14-18 June 2009, Dresden, Germany.

Shabtai, A., Kanonov, U., Elovici, Y., Glezer, G., & Weiss, Y. (2012), “Andromaly”: A behavioral malware detection framework for Android devices. Journal of Intelligent Information Systems, 38(1): 161-190.

Wu, D., Mao, C., Wei, T., Lee, H., & Wu, K. (2012). DroidMat: Android malware detection through manifest and API calls tracing. 2012 Seventh Asia Joint Conference on Information Security, 9-10 August 2012, Tokyo, Japan.

Yang, C., Yegneswaran, V., Porras, P., & Gu, G. (2012). POSTER: Detecting money-stealing apps in alternative Android markets. CCS '12 Proceedings of the 2012 ACM Conference on Computer and Communications Security, 16-18 October 2012, Raleigh, North Carolina, USA.

Zhou, Y., Wang, Z., Zhou, W., & Jiang, X. (2012). Hey, you, get off of my market: Detecting malicious apps in official and alternative Android markets. Proceedings of the 19th Annual Network and Distributed System Security Symposium, 5-8 February 2012, San Diego, California, USA.

DOI

https://doi.org/10.15394/jdfsl.2013.1149

 

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