Mobile devices that run Android operating system are widely used. The applications running in Android mobiles can have malicious permissions due to malware. In other words, Android applications might spread malware which can sabotage valuable data. Therefore it is essential to have mechanism to classify malware and benign mobile applications running in Android phones. Since Android mobile applications run in the confines of mobile devices and associated servers, it is very challenging task to detect Android malware. Many solutions came into existence to detect malware applications. Of late Abawajy et al. proposed a technique known as Iterative Classifier Fusion System (ICFS) which employs classifiers iteratively with fusion to generate a final classifier for effective detection of malware. They combined NB tree classifier, Multilayer perception and Lib SVM with polynomial kernel to achieve this. However, the system does not focus on reduction or pruning of Android application permissions so as to build a classifier that reduces time and space complexity. In the proposed system, a methodology is proposed that focuses on reduction or pruning of android application permissions and ranking them in order to build a classifier that reduces time and space complexity. The classifier modelled with best ranked permissions can be representative of all permissions as least significant permissions are pruned to reduce search space. This paper built a prototype application to demonstrate proof of the concept. The experimental results revealed that the proposed system performs better in improving detection accuracy besides precision and recall measures.

Malware, malware detection technique, pruning, ranking

[1] P. Faruki, A. Bharmal, V. Laxmi, V. Ganmoor, M. S. Gaur, M. Conti, and M. Rajarajan, “Android security: A survey of issues, malware penetration, and defences,” IEEE Communications Surveys and Tutorials, vol. 17, pp. 998–1022, 2015.
[2] Y. Zhou and X. Jiang, “Dissecting Android malware: Characterization and evolution,” in Proceedings of the 33rd IEEE Symposium on Security and Privacy, San Francisco, CA, pp. 95–109, 2012.
[3] J. Walls and K.-K. R. Choo, “A review of free cloud-based antimalware apps for Android,” in Proceedings of 2015 14th IEEE International Conference on Trust, Security and Privacy in Computing and Communications, Trust Com 2015, vol. 1, pp. 1053–1058, 2015.
[4] S. Naval, V. Laxmi, M. Rajarajan, M. S. Gaur, and M. Conti, “Employing program semantics for malware detection,” IEEE Transactions on Information Forensics and Security, vol. 10, no. 12, pp. 2591–2604, 2015.
[5] P. Faruki, S. Bhandari, V. Laxmi, M. Gaur, and M. Conti, “Droid analyst : Synergic app framework for static and dynamic app analysis,” Studies in Computational Intelligence, vol. 621, pp. 519–552, 2015.
[6] L. Sinha, S. Bhandari, P. Faruki, M. S. Gaur, V. Laxmi, and M. Conti, “Flow Mine : Android app analysis via data flow,” in Proceeding of the 13th IEEE Annual Consumer Communications and Networking Conference, CCNC 2016, pp. 435–441, 2016.
[7] J. Abawajy, M. Chowdhury, and A. Kelarev, ”Hybrid Consensus Pruning of Ensemble Classifiers for Big Data Malware Detection,” IEEE Transaction on Cloud Com put 3(2):111, 2017.
[8] S. Sheen, R. Anitha, and V. Natarajan, “Android based malware detection using a multi feature collaborative decision fusion approach,” Neuro computing, vol. 151, pp. 905–912, 2015.
[9] S. Naval, V. Laxmi, M. S. Gaur, S. Raja, M. Rajarajan, and M. Conti, “Environment-reactive malware behaviour: Detection and categorization,” in Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance, ser. LNCS, vol. 8872, pp. 167–182, 2015.
[10] F. Daryabar, A. Dehghantanha, F. Norouzi, and F. Mahmoodi, “Analysis of virtual honey net and vlan-based virtual networks,” in International Symposium on Humanities, Science and Engineering Research, SHUSER 2011, pp. 73–77, 2011.
[11] K. Zhao, D. Zhang, X. Su, and W. Li, “Fest: A feature extraction and selection tool for Android malware detection,” in 20th IEEE Symposium on Computers and Communication, ISCC 2015, pp. 714–720, 2015
[12] J. Abawajy, A. Kelarev “Iterative Classifier Fusion System for the Detection of Android Malware”. IEEE Transactions on Big Data, Vol. 5, No. 4, p1-12, 2017.