• DocumentCode
    682680
  • Title

    Input data preprocessing for bots detection using the dendritic cells algorithm

  • Author

    Xianjin Fang ; Jia Liu

  • Author_Institution
    Sch. of Comput. Sci. & Eng., Anhui Univ. of Sci. & Technol., Huainan, China
  • Volume
    03
  • fYear
    2013
  • fDate
    16-18 Dec. 2013
  • Firstpage
    1362
  • Lastpage
    1366
  • Abstract
    The dendritic cells algorithm (DCA) is an immune-inspired intelligent algorithm and is based on an abstract model of the behavior of dendritic cells (DCs) in immunology. Meanwhile, a number of bots based on diverse protocols, including P2P, IRC, DNS and HTTP, have become an increasing threat to network security. So many researchers use the DCA to detect a single bot in computer system. But the DCA´s input is the time series data consisting of antigens and signals. The complex raw data must be preprocessed to form the input stream of antigens and signals. The data preprocessing of the DCA includes raw data collection phase, selection, extraction and mapping phase of antigens and signals. This paper proposed the several methods of input data preprocessing for bots detection using the DCA. These methods are suit for the different bots detection, such as IRC and P2P bots detection.
  • Keywords
    computer network security; hypermedia; peer-to-peer computing; time series; transport protocols; DCA; DNS; HTTP; IRC bots detection; P2P bots detection; antigens; complex raw data; computer network security; data collection; dendritic cells algorithm; diverse protocols; immune-inspired intelligent algorithm; input data preprocessing; time series data; Data preprocessing; Educational institutions; Immune system; Monitoring; Protocols; Sockets; Time series analysis; antigen; bots detection; data preprocessing; dendritic cells algorithm; signal;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image and Signal Processing (CISP), 2013 6th International Congress on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-1-4799-2763-0
  • Type

    conf

  • DOI
    10.1109/CISP.2013.6743885
  • Filename
    6743885