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Multi-agent Artificial Immune System for Network Intrusion Detection and Classification

  • Amira Sayed A. AzizEmail author
  • Sanaa El-Ola Hanafi
  • Aboul Ella Hassanien
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 299)

Abstract

A multi-agent artificial immune system for network intrusion detection and classification is proposed and tested in this paper. The multi-layer detection and classification process is proposed to be executed on each agent, for each host in the network. The experiment shows very good results in detection layer, where 90% of anomalies are detected. For the classification layer, 88% of false positives were successfully labeled as normal traffic connections, and 79% of DoS and Probe attacks were labeled correctly. An analysis is given for future work to enhance results for low-presented attacks.

Keywords

Data Item Intrusion Detection Anomaly Detection Main Agent Detector Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Amira Sayed A. Aziz
    • 1
    • 3
    Email author
  • Sanaa El-Ola Hanafi
    • 2
  • Aboul Ella Hassanien
    • 2
    • 3
  1. 1.Université Française d’ÉgypteCairoEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityCairoEgypt
  3. 3.Scientific Research Group in Egypt (SRGE)CairoEgypt

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