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An auto-learning approach for network intrusion detection

Author

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  • Ammar Boulaiche

    (University de Bejaia
    University Of Quebec in Outaouais)

  • Kamel Adi

    (University Of Quebec in Outaouais)

Abstract

In this paper, we propose a novel intrusion detection technique with a fully automatic attack signatures generation capability. The proposed approach exploits a honeypot traffic data analysis to build an attack scenarios database, used to detect potential intrusions. Furthermore, for an effective and efficient intrusion detection mechanism, we introduce several new or adapted algorithms for signature generation, signature comparison, etc. Finally, we use DARPA’99 and UNSW-NB15 traffic to evaluate the proposed approach. The results indicate that the generated attack signatures are of high quality with low rates of false negatives and false positives.

Suggested Citation

  • Ammar Boulaiche & Kamel Adi, 2018. "An auto-learning approach for network intrusion detection," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(2), pages 277-294, June.
    • Handle: RePEc:spr:telsys:v:68:y:2018:i:2:d:10.1007_s11235-017-0395-z
    DOI: 10.1007/s11235-017-0395-z
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    Cited by:

    1. El Mehdi Kandoussi & Mohamed Hanini & Iman Mir & Abdelkrim Haqiq, 2020. "Toward an integrated dynamic defense system for strategic detecting attacks in cloud networks using stochastic game," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 73(3), pages 397-417, March.

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