Citation
Adrian Lauf, Richard A. Peters, William H. Robinson. "Embedded Intelligent Intrusion Detection: A Behavior-Based Approach". IEEE 4th international symposium on Embedded Computing, IEEE, N/A, May, 2007.

Abstract
This paper describes the development of an intelligent intrusion detection system for use within an embedded device network consisting of interconnected agents. Integral behavior types are categorized by focusing primarily on inter-device requests and actions rather than at a packet or link level. Machine learning techniques use these observed behavioral actions to track devices which deviate from normal protocol. Deviant behavior can be analyzed and flagged, enabling interconnected agents to identify an intruder based upon the historical distribution of behavioral data that is accumulated about the possible deviant agent. Simulation results from the prototype system correlate detection accuracy with a tunable input tolerance factor.

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Citation formats

• HTML

  Adrian Lauf, Richard A. Peters, William H. Robinson. <a
  href="http://www.truststc.org/pubs/187.html">Embedded
  Intelligent Intrusion Detection: A Behavior-Based
  Approach</a>, IEEE 4th international symposium on
  Embedded Computing, IEEE, N/A, May, 2007.

• Plain text

  Adrian Lauf, Richard A. Peters, William H. Robinson.
  "Embedded Intelligent Intrusion Detection: A Behavior-Based
  Approach". IEEE 4th international symposium on Embedded
  Computing, IEEE, N/A, May, 2007.

• BibTeX

  @inproceedings{LaufPetersRobinson07_EmbeddedIntelligentIntrusionDetectionBehaviorBased,
      author = {Adrian Lauf and Richard A. Peters and William H.
                Robinson},
      title = {Embedded Intelligent Intrusion Detection: A
                Behavior-Based Approach},
      booktitle = {IEEE 4th international symposium on Embedded
                Computing},
      organization = {IEEE},
      pages = {N/A},
      month = {May},
      year = {2007},
      abstract = {This paper describes the development of an
                intelligent intrusion detection system for use
                within an embedded device network consisting of
                interconnected agents. Integral behavior types are
                categorized by focusing primarily on inter-device
                requests and actions rather than at a packet or
                link level. Machine learning techniques use these
                observed behavioral actions to track devices which
                deviate from normal protocol. Deviant behavior can
                be analyzed and flagged, enabling interconnected
                agents to identify an intruder based upon the
                historical distribution of behavioral data that is
                accumulated about the possible deviant agent.
                Simulation results from the prototype system
                correlate detection accuracy with a tunable input
                tolerance factor.},
      URL = {http://www.truststc.org/pubs/187.html}
  }
  

Posted by Adrian Lauf on 1 Mar 2007.
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