• DocumentCode
    153023
  • Title

    Analysis of machine learning methods on malware detection

  • Author

    Aydogan, Emre ; Sen, Satyaki

  • Author_Institution
    Bilgisayar Muhendisligi Bolumu, Hacettepe Univ., Ankara, Turkey
  • fYear
    2014
  • fDate
    23-25 April 2014
  • Firstpage
    2066
  • Lastpage
    2069
  • Abstract
    Nowadays, one of the most important security threats are new, unseen malicious executables. Current anti-virus systems have been fairly successful against known malicious softwares whose signatures are known. However they are very ineffective against new, unseen malicious softwares. In this paper, we aim to detect new, unseen malicious executables using machine learning techniques. We extract distinguishing structural features of softwares and, employ machine learning techniques in order to detect malicious executables.
  • Keywords
    invasive software; learning (artificial intelligence); anti-virus systems; machine learning methods; malicious executables detection; malicious softwares; malware detection; security threats; software structural features; Conferences; Internet; Malware; Niobium; Signal processing; Software; machine learning; malware analysis and detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing and Communications Applications Conference (SIU), 2014 22nd
  • Conference_Location
    Trabzon
  • Type

    conf

  • DOI
    10.1109/SIU.2014.6830667
  • Filename
    6830667