• DocumentCode
    694603
  • Title

    LAYSEE: Learn-as-you-see traffic classifier

  • Author

    Tongaonkar, Alok ; Iliofotou, Marios ; Keralapura, Ram

  • Author_Institution
    Narus Inc., Sunnyvale, CA, USA
  • fYear
    2013
  • fDate
    14-19 April 2013
  • Firstpage
    25
  • Lastpage
    26
  • Abstract
    The ability to classify all traffic that traverses a network is a critical aspect of network management. Signature based traffic classifiers are widely used to provide that capability. The state of the art classifiers rely on static, manual, and tedious approach of protocol reverse engineering to obtain signatures. However, the explosion of never-seen-before applications on the internet has resulted in a drastic reduction in the effectiveness of such systems. To overcome these limitations, we have developed a novel system, called Learn-As-You-SEE (LAYSEE), that aims to provide dynamic, automated, and exhaustive application identification. Our system automatically extracts signatures from network traffic by leveraging the benefits of packet content signature inference techniques and sophisticated behavioral-based analysis. These signatures are used for classifying subsequent traffic.
  • Keywords
    Internet; computer network management; pattern classification; protocols; reverse engineering; telecommunication traffic; Internet; LAYSEE; automated application identification; behavioral-based analysis; dynamic application identification; exhaustive application identification; learn-as-you-see traffic classifier; network management; network traffic; never-seen-before applications; packet content signature inference techniques; protocol reverse engineering; signature based traffic classifiers; signature extraction; Classification algorithms; Feature extraction; Internet; Manuals; Payloads; Ports (Computers); Protocols;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Communications Workshops (INFOCOM WKSHPS), 2013 IEEE Conference on
  • Conference_Location
    Turin
  • Print_ISBN
    978-1-4799-0055-8
  • Type

    conf

  • DOI
    10.1109/INFCOMW.2013.6970707
  • Filename
    6970707