文章基本信息

标题：A Study Of Machine Learning Classifiers for Anomaly-Based Mobile Botnet Detection
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作者：Ali Feizollah ; Nor Badrul Anuar ; Rosli Salleh 等
期刊名称：Malaysian Journal of Computer Science
印刷版ISSN：0127-9084
出版年度：2013
卷号：26
期号：4
出版社：University of Malaya * Faculty of Computer Science and Information Technology
摘要：In recent years, mobile devices are ubiquitous. They are employed for purposes beyond merely making phone calls. Among the mobile operating systems, Android is the most popular due to its availability as an open source operating system. Due to the proliferation of Android malwares, it is crucial to study the best classifiers that can detect these malwares effectively and accurately through selecting the most suitable network traffic features as well as comprehensive comparison with related works. This study evaluates five machine learning classifiers, namely Naïve Bayes, knearest neighbour, decision tree, multilayer perceptron, and support vector machine. The evaluation was validated using malware data samples from the Android Malware Genome Project. The data sample is a collection of malwares gathered between August 2010 and October 2011 by the University of North Carolina. Among various network traffic characteristics, three network features were selected: connection duration, TCP size and number of GET/POST parameters. From the experiment, it is found that knearest neighbour provides the optimum results in terms of performance among the classifiers. The experimental results also indicate a true positive rate as high as 99.94% and false positive of 0.06% for the knearest neighbour classifier.
关键词：Mobile botnet; machine learning classifiers; anomalybased detection; intrusion detection systems