• DocumentCode
    1876843
  • Title

    Cloud-based Android botnet malware detection system

  • Author

    Jadhav, Suyash ; Dutia, Shobhit ; Calangutkar, Kedarnath ; Tae Oh ; Young Ho Kim ; Joeng Nyeo Kim

  • Author_Institution
    Dept. of Inf. Sci. & Technol., Rochester Inst. of Technol., Rochester, NY, USA
  • fYear
    2015
  • fDate
    1-3 July 2015
  • Firstpage
    347
  • Lastpage
    352
  • Abstract
    Increased use of Android devices and its open source development framework has attracted many digital crime groups to use Android devices as one of the key attack surfaces. Due to the extensive connectivity and multiple sources of network connections, Android devices are most suitable to botnet based malware attacks. The research focuses on developing a cloud-based Android botnet malware detection system. A prototype of the proposed system is deployed which provides a runtime Android malware analysis. The paper explains architectural implementation of the developed system using a botnet detection learning dataset and multi-layered algorithm used to predict botnet family of a particular application.
  • Keywords
    Android (operating system); cloud computing; invasive software; smart phones; botnet detection learning dataset; cloud-based Android botnet malware detection system; digital crime groups; multilayered algorithm; open source development framework; runtime Android malware analysis; Androids; Classification algorithms; Feature extraction; Humanoid robots; Java; Malware; Servers; Android Sandbox; Android botnet; Android botnet family detection; Android on VirtualBox; Cloud-based malware detection; Vyatta;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Communication Technology (ICACT), 2015 17th International Conference on
  • Conference_Location
    Seoul
  • Print_ISBN
    978-8-9968-6504-9
  • Type

    conf

  • DOI
    10.1109/ICACT.2015.7224817
  • Filename
    7224817