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Mobile Malware Detection using Op-code Frequency Histograms by Gerardo Canfora, Francesco Mercaldo and Corrado Aaron Visaggio

pubblicato 28 mag 2015, 13:18 da Gerardo Canfora   [ aggiornato in data 19 feb 2016, 13:27 ]
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 iden- tifying 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%.
12th International Conference on Security and Cryptography - SECRYPT 2015
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Gerardo Canfora,
28 mag 2015, 13:20
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