In this paper, we evaluate the state-of-the-art commercial mobile anti-malware products for Android and test how resistant they are against various common obfuscation techniques (even with known malware). Mobile malware threats (e.g., on Android) have recently become a real concern. Results show that an accuracy of 97\% can be obtained on the average, whereas perfect detection rate is achieved for more than one malware family. Then, we experimentally evaluated it on a recent dataset composed of 11120 applications, 5560 of which are malware belonging to several different families. To this end, we designed a method based on state-of-the-art classifiers applied to frequencies of opcodes $n$grams. In this paper, we investigate if frequencies of $n$grams of opcodes are effective in detecting Android malware and if there is some significant malware family for which they are more or less effective. Some works in the literature showed that opcodes are informative for detecting malware, not only in the Android platform. With the wide diffusion of smartphones and their usage in a plethora of processes and activities, these devices have been handling an increasing variety of sensitive resources.Īttackers are hence producing a large number of malware applications for Android (the most spread mobile platform), often by slightly modifying existing applications, which results in malware being organized in families.
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