Android malware detection: An eigenspace analysis approach
The battle to mitigate Android malware has become more critical with the emergence of new strains incorporating increasingly sophisticated evasion techniques, in turn necessitating more advanced detection capabilities. Hence, in this paper we propose and evaluate a machine learning based approach based on eigenspace analysis for Android malware detection using features derived from static analysis characterization of Android applications. Empirical evaluation with a dataset of real malware and benign samples show that detection rate of over 96% with a very low false positive rate is achievable using the proposed method.
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Citation : Yerima, S. Y., Sezer, S., Muttik, I. (2015) Android Malware Detection: An Eigenspace Analysis Approach. In: Proceedings of the 2015 Science and Information Conference (SAI), London, UK, 28-30 July 2015, pp. 1236-1242
Research Group : Cyber Technology Institute (CTI)
Research Institute : Cyber Technology Institute (CTI)
Peer Reviewed : Yes