• DocumentCode
    3703978
  • Title

    Identifying Unknown Android Malware with Feature Extractions and Classification Techniques

  • Author

    Ludovic Apvrille;Axelle Apvrille

  • Author_Institution
    Inst. Mines-Telecom, Telecom ParisTech, Sophia Antipolis, France
  • Volume
    1
  • fYear
    2015
  • Firstpage
    182
  • Lastpage
    189
  • Abstract
    Android malware unfortunately have little difficulty to sneak in marketplaces. While known malware and their variants are nowadays quite well detected by antivirus scanners, new unknown malware, which are fundamentally different from others (e.g. "0-day"), remain an issue. To discover such new malware, the SherlockDroid framework filters masses of applications and only keeps the most likely to be malicious for future inspection by antivirus teams. Apart from crawling applications from marketplaces, SherlockDroid extracts code-level features, and then classifies unknown applications with Alligator. Alligator is a classification tool that efficiently and automatically combines several classification algorithms. To demonstrate the efficiency of our approach, we have extracted properties and classified over 600,000 applications during two crawling campaigns in July 2014 and October 2014, with the detection of one new malware, Android/Odpa.A!tr.spy, and two new riskware. With other findings, this increases SherlockDroid´s "Hall of Shame" to 9 totally unknown malware and potentially unwanted applications.
  • Keywords
    "Feature extraction","Malware","Androids","Humanoid robots","Heuristic algorithms","Algorithm design and analysis","Support vector machines"
  • Publisher
    ieee
  • Conference_Titel
    Trustcom/BigDataSE/ISPA, 2015 IEEE
  • Type

    conf

  • DOI
    10.1109/Trustcom.2015.373
  • Filename
    7345281