A review on feature selection in mobile malware detection. (June 2015)

• Record Type: Journal Article
• Authors: Feizollah, Ali; Anuar, Nor Badrul; Salleh, Rosli; Wahab, Ainuddin Wahid Abdul
• Is Part Of: Digital investigation. Volume 13(2015)
• Page Start: 22
• Page End: 37
• Publication Date: 2015-06
• Subjects: Mobile malware -- Android -- Feature selection -- Review paper -- Mobile operating system; Forensic sciences -- Data processing -- Periodicals; Criminal investigation -- Data processing -- Periodicals; 363.250285
• DOI: 10.1016/j.diin.2015.02.001
• Languages: English
• ISSNs: 1742-2876

Abstract

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.