Optimizing Machine Learning based Large Scale Android Malwares Detection by Feature Disposal
As a favorable opening platform for mobile terminals, android platform attracts close attentions from a large number of hackers. The great potential of security hazard makes the requirement of malicious software detection become effective, rapid and multitudinous. In recent years, a lot of machine learning based methods have been proposed. However, most of the focuses are searching for more effective feature information. In this paper, we propose an optimization method for machine-learning-based malware detection by focusing on the disposal of the feature information. We extract permission and intent information of malwares, and dispose them in a series of effectively methods. After the disposal, we use several machine learning algorithms to verify their effectiveness, and conclude a comparing list. After the comparing, we propose an optimization algorithm by combining several effective processing. The effectiveness of our proposal is illustrated and justified in extensive experimental results.
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