• DocumentCode
    16749
  • Title

    Automatically mining application signatures for lightweight deep packet inspection

  • Author

    Lu Gang ; Zhang Hongli ; Zhang Yu ; Qassrawi, M.T. ; Yu Xiangzhan ; Peng Lizhi

  • Author_Institution
    Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China
  • Volume
    10
  • Issue
    6
  • fYear
    2013
  • fDate
    Jun-13
  • Firstpage
    86
  • Lastpage
    99
  • Abstract
    Automatic signature generation approaches have been widely applied in recent traffic classification. However, they are not suitable for LightWeight Deep Packet Inspection (LW_DPI) since their generated signatures are matched through a search of the entire application data. On the basis of LW_DPI schemes, we present two Hierarchical Clustering (HC) algorithms: HC_TCP and HC_UDP, which can generate byte signatures from TCP and UDP packet payloads respectively. In particular, HC_TCP and HC_ UDP can extract the positions of byte signatures in packet payloads. Further, in order to deal with the case in which byte signatures cannot be derived, we develop an algorithm for generating bit signatures. Compared with the LASER algorithm and Suffix Tree (ST)-based algorithm, the proposed algorithms are better in terms of both classification accuracy and speed. Moreover, the experimental results indicate that, as long as the application-protocol header exists, it is possible to automatically derive reliable and accurate signatures combined with their positions in packet payloads.
  • Keywords
    Internet; data mining; inspection; telecommunication traffic; transport protocols; HC_TCP; HC_UDP; LASER algorithm; LW_DPI; application protocol header; application signatures; automatic signature generation; byte signatures; classification accuracy; hierarchical clustering; lightweight deep packet inspection; packet payloads; traffic classification; Classification algorithms; Clustering algorithms; Machine learning algorithms; Payloads; Ports (Computers); Telecommunication traffic; Training; LW_DPI; association mining; automatic signature generation; hierarchical clustering; traffic classification;
  • fLanguage
    English
  • Journal_Title
    Communications, China
  • Publisher
    ieee
  • ISSN
    1673-5447
  • Type

    jour

  • DOI
    10.1109/CC.2013.6549262
  • Filename
    6549262