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• W2949992015 abstract "Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance techniques and this calls for more effective approaches for Android malware detection. Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. This approach enables automated feature discovery without relying on prior expert or domain knowledge for pre-determined features. Furthermore, by using a data segmentation technique for feature selection, our analysis is able to scale up to 10-gram opcodes. Our experiments on a dataset of 2520 samples showed an f-measure of 98% using the n-gram opcode based approach. We also provide empirical findings that illustrate factors that have probable impact on the overall n-gram opcodes performance trends." @default.
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• W2949992015 date "2016-11-07" @default.
• W2949992015 modified "2023-09-25" @default.
• W2949992015 title "N-gram Opcode Analysis for Android Malware Detection" @default.
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• W2949992015 doi "https://doi.org/10.22619/ijcsa.2016.100111" @default.
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