Introduction

Zhou and Jiang [1] extensively surveyed and analyzed Android malware and found that 86% of the malware collected incorporated repackaged benign applications, and that many of them utilized common advertisement libraries. Such benign code reuse in malware can be expected to cause automated classification and clustering approaches to fail if they base their decisions on features relating to the reused code. To improve detection, classification, and clustering, feature selection from mobile malware must not be naïve, but must instead utilize knowledge of malicious program semantics and structure. We propose an approach for selecting features of mobile malware by using knowledge of malicious program structure to heuristically identify malicious portions of applications.

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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2012

Authors and Affiliations

  • Andrew Walenstein
    • 1
  • Luke Deshotels
    • 2
  • Arun Lakhotia
    • 2
  1. 1.Center for Advanced Computer StudiesUniversity of Louisiana at LafayetteLafayetteUSA
  2. 2.School of Computing and InformaticsUniversity of Louisiana at LafayetteLafayetteUSA

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