• DocumentCode
    3658054
  • Title

    Potential Component Leaks in Android Apps: An Investigation into a New Feature Set for Malware Detection

  • Author

    Li Li;Kevin Allix;Daoyuan Li;Alexandre Bartel;Tegawendé F. Bissyandé;Jacques Klein

  • Author_Institution
    SnT, Univ. of Luxembourg, Luxembourg, Luxembourg
  • fYear
    2015
  • Firstpage
    195
  • Lastpage
    200
  • Abstract
    We discuss the capability of a new feature set for malware detection based on potential component leaks (PCLs). PCLs are defined as sensitive data-flows that involve Android inter-component communications. We show that PCLs are common in Android apps and that malicious applications indeed manipulate significantly more PCLs than benign apps. Then, we evaluate a machine learning-based approach relying on PCLs. Experimental validations show high performance for identifying malware, demonstrating that PCLs can be used for discriminating malicious apps from benign apps.
  • Keywords
    "Malware","Androids","Humanoid robots","Feature extraction","Libraries","Machine learning algorithms","Training"
  • Publisher
    ieee
  • Conference_Titel
    Software Quality, Reliability and Security (QRS), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/QRS.2015.36
  • Filename
    7272932