Permission-Based Malware Detection System for Android Using Machine Learning Techniques


Arslan R. S. , DOĞRU İ. A. , BARIŞÇI N.

INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING, vol.29, no.1, pp.43-61, 2019 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 1
  • Publication Date: 2019
  • Doi Number: 10.1142/s0218194019500037
  • Title of Journal : INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING
  • Page Numbers: pp.43-61
  • Keywords: Android, permission-based, security, risk assessment, malware detection, MOBILE, SECURITY, VERIFICATION, THREATS

Abstract

Mobile applications create their own security and privacy models through permission-based models. Some applications may request extra permissions that they do not need but may use for suspicious activities. The aim of this study is to identify those spare permissions requested and use this information in the security and privacy approach, which uses static and code analysis together and applies them to the existing datasets; then the results are compared and accuracy level is determined. Classification is made with an accuracy rate of 91.95%.