The ineffectiveness of signature-based malware detection systems prevents the detection of malware, even objects of trivial obfuscation techniques, makes mobile devices vulnerable. In this paper a dynamic technique to detect malware on Android platform is proposed. We exploit a set of energy related features i.e., feature which can be symptomatic of abnormal battery consumption. We built different models exploiting four different supervised machine learning classification algorithms, obtaining for all the evaluated models an accuracy greater than 0.91.
Energy consumption metrics for mobile device dynamic malware detection
Fasano F.;Mercaldo F.;Santone A.
2019-01-01
Abstract
The ineffectiveness of signature-based malware detection systems prevents the detection of malware, even objects of trivial obfuscation techniques, makes mobile devices vulnerable. In this paper a dynamic technique to detect malware on Android platform is proposed. We exploit a set of energy related features i.e., feature which can be symptomatic of abnormal battery consumption. We built different models exploiting four different supervised machine learning classification algorithms, obtaining for all the evaluated models an accuracy greater than 0.91.File in questo prodotto:
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