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Application Marketplace Malware Detection by User Feedback Analysis

Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 867)

Abstract

Smartphones are becoming increasingly ubiquitous. Like recommended best practices for personal computers, users are encouraged to install antivirus and intrusion detection software on their mobile devices. However, even with such software these devises are far from being fully protected. Given that application stores are the source of most applications, malware detection on these platforms is an important issue. Based on our intuition, which suggests that an application’s suspicious behavior will be noticed by some users and influence their feedback, we present an approach for analyzing user reviews in mobile application stores for the purpose of detecting malicious apps. The proposed method transfers an application’s text reviews to numerical features in two main steps: (1) extract domain-phrases based on external domain-specific textual corpus on computer and network security, and (2) compute three statistical features based on domain-phrases occurrences. We evaluated the proposed methods on 2,506 applications along with their 128,863 reviews collected from “Amazon AppStore”. The results show that proposed method yields an AUC of 86% in the detection of malicious applications.

Keywords

Mobile malware Malware detection User feedback analysis Text mining Review mining 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Software and Information Systems EngineeringBen-Gurion University of the NegevBeershebaIsrael

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