Crowdroid : Behavior-Based Malware Detection System for Android Categories and Subject Descriptors

Abstract

The sharp increase in the number of smartphones on the market, with the Android platform posed to becoming a market leader makes the need for malware analysis on this platform an urgent issue. In this paper we capitalize on earlier approaches for dy- namic analysis of application behavior as a means for detect- ing malware in the Android platform. The detector is em- bedded in a overall framework for collection of traces froman unlimited number of real users based on crowdsourcing. Our framework has been demonstrated by analyzing the data collected in the central server using two types of data sets: those from artificial malware created for test purposes, and those from real malware found in the wild. The method is shown to be an effective means of isolating the malware and alerting the users of a downloaded malware. This shows the potential for avoiding the spreading of a detected malware to a larger community.