Mobile Malware Detection using Op-code Frequency Histograms
Gerardo Canfora, Francesco Mercaldo, Corrado Aaron Visaggio · 2015
Mobile malware has grown in scale and complexity, as a consequence of the unabated uptake of smartphones worldwide. Malware writers have been developing detection evasion techniques which are rapidly making anti-malware technologies uneffective. In particular, zero-days malware is able to easily pass signature based detection, while dynamic analysis based techniques, which could be more accurate and robust, are too costly or inappropriate to real contexts, especially for reasons related to usability. This paper discusses a technique for discriminating Android malware from trusted applications that does not rely on signature, but on identifying a vector of features obtained from the static analysis of the Android's Dalvik code. Experimentation accomplished on a sample of 11,200 applications revealed that the proposed technique produces high precision (over 93%) in mobile malware detection, with an accuracy of 95%.