Android Malware Detection: an Eigenspace Analysis Approach
This addresses the problem of detecting sophisticated Android malware for security applications, but appears incremental as it applies an existing mathematical technique to this domain.
The paper tackled Android malware detection by proposing a machine learning approach based on eigenspace analysis using static analysis features, achieving a detection rate of over 96% with a very low false positive rate in empirical evaluation.
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.