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Mobile Malware Detection in the Real World by Francesco Mercaldo, Corrado Aaron Visaggio, Gerardo Canfora, Aniello Cimitile

pubblicato 25 feb 2016, 13:30 da Gerardo Canfora   [ aggiornato in data 25 feb 2016, 13:41 ]

Several works in literature address the mobile malware detection problem by classifying features obtained from real world application and using well-known machine-learning techniques. Several authors have published empirical studies aimed at assessing the quality of set of features. In this paper we propose BehaveYourself!, an Android application able to discriminate a trusted application by a malicious one extracting opcode-based features. Our application is open and flexible: it can be used as a starting point to define, and experiment with, additional features. We release BehaveYour- self! to the research community at the following url:
A poster at ICSE-2016
Gerardo Canfora,
25 feb 2016, 13:31