A review on feature selection in mobile malware detection |
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| Institution: | 1. School of Mathematics & Engineering, City University London, London EC1V 0HB, UK;2. Department of Mathematics, University of Padua, 35122 Padova, Italy;3. Institute for Digital Technologies, Loughborough University in London, London, UK;1. School of Information Engineering, Yang Zhou University, Yang Zhou 225000, China;2. The Xinjiang Technical Institute of Physics and Chemistry, Chinese Academy of Sciences, Urumqi 830011, China;3. Xinjiang Laboratory of Minority Speech and Language Information Processing, Urumqi 830011, China;4. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China;5. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China;1. Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, 50603 Kuala Lumpur, Malaysia;2. Computer Security (COSEC) Lab, Department of Computer Science, Universidad Carlos III de Madrid, 28911 Leganes, Madrid, Spain;3. Centre for Security, Communications and Network Research, School of Computing, Electronics and Mathematics, Plymouth University, Drake Circus, Plymouth PL4 8AA, UK |
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| Abstract: | The widespread use of mobile devices in comparison to personal computers has led to a new era of information exchange. The purchase trends of personal computers have started decreasing whereas the shipment of mobile devices is increasing. In addition, the increasing power of mobile devices along with portability characteristics has attracted the attention of users. Not only are such devices popular among users, but they are favorite targets of attackers. The number of mobile malware is rapidly on the rise with malicious activities, such as stealing users data, sending premium messages and making phone call to premium numbers that users have no knowledge. Numerous studies have developed methods to thwart such attacks. In order to develop an effective detection system, we have to select a subset of features from hundreds of available features. In this paper, we studied 100 research works published between 2010 and 2014 with the perspective of feature selection in mobile malware detection. We categorize available features into four groups, namely, static features, dynamic features, hybrid features and applications metadata. Additionally, we discuss datasets used in the recent research studies as well as analyzing evaluation measures utilized. |
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| Keywords: | Mobile malware Android Feature selection Review paper Mobile operating system |
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