• DocumentCode
    529614
  • Title

    Detection technology for unknown virus based on data farming

  • Author

    Shi Hao-bin ; Li Wen-Bin

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Northwestern Polytech. Univ., Xi´an, China
  • Volume
    1
  • fYear
    2010
  • fDate
    28-31 Aug. 2010
  • Firstpage
    96
  • Lastpage
    99
  • Abstract
    In order to improve the detection rate of unknown virus, this paper presented a detection technology for unknown computer virus based on data farming, which synthetically considered the types of viruses, feature extraction methods for viruses of different types, data classification algorithms and other factors. Firstly, this technology extracted the behavior feature of the files to be executed, and then analyzed the extracted feature data by means of improved data farming technology which absorbed naive bayes classification algorithm to detect whether the file to be executed contained viruses. Experimental results showed that this technology had the higher accuracy and lower error rate in unknown computer viruses detection.
  • Keywords
    Bayes methods; computer viruses; feature extraction; pattern classification; behavior feature; data classification algorithms; data farming; error rate; feature extraction methods; naive bayes classification algorithm; unknown computer viruses detection; Accuracy; Buildings; Classification algorithms; Computational modeling; data farming; naive bayes; unknown virus; virus detection;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing (IITA-GRS), 2010 Second IITA International Conference on
  • Conference_Location
    Qingdao
  • Print_ISBN
    978-1-4244-8514-7
  • Type

    conf

  • DOI
    10.1109/IITA-GRS.2010.5602957
  • Filename
    5602957