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False Alarm Reduction Using Adaptive Agent-Based Profiling

Author

Listed:
  • Salima Hacini

    (Lire Laboratory, TLSI Department, Constantine2 University, Constantine, Algeria)

  • Zahia Guessoum

    (LIP6, Pierre et Marie Curie University, Paris, France)

  • Mohamed Cheikh

    (Lire Laboratory, TLSI Department, Constantine2 University, Constantine, Algeria)

Abstract

In this paper the authors propose a new efficient anomaly-based intrusion detection mechanism based on multi-agent systems. New networks are particularly vulnerable to intrusion, they are often attacked with intelligent and skilful hacking techniques. The intrusion detection techniques have to deal with two problems: intrusion detection and false alarms. The issue of false alarms has an important impact on the success of the anomaly-based intrusion detection technologies. The purpose of this paper is to improve their accuracy by detecting real attacks and by reducing the number of unnecessary generated alerts. The authors' intrusion detection mechanism relies on a set of agents to ensure the detection and the adaptation of normal profile to support the legitimate dynamic changes that occur and are the cause of high rate of false alarms.

Suggested Citation

  • Salima Hacini & Zahia Guessoum & Mohamed Cheikh, 2013. "False Alarm Reduction Using Adaptive Agent-Based Profiling," International Journal of Information Security and Privacy (IJISP), IGI Global, vol. 7(4), pages 53-74, October.
    • Handle: RePEc:igg:jisp00:v:7:y:2013:i:4:p:53-74
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