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Stochastic Local Search Based Feature Selection for Intrusion Detection

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Artificial Intelligence XXXVI (SGAI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11927))

Abstract

Intrusion detection is the ability to mitigate attacks and block new threats. In this paper, we deal with intrusion detection as a pattern classification problem where a connection is defined as a set of attributes. The latter forms a pattern that should be assigned to one of existing classes. The problem is to identify the given connection as a normal event or attack. We propose a stochastic local search method for feature selection where the aim is to select the set of significant attributes to be used in the classification task. The proposed approach is validated on the well-known NLS-KDD dataset and compared with some existing techniques. The results are interesting and show the efficiency of the proposed approach for intrusion detection.

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Author information

Authors and Affiliations

  1. LRIA-FEI- USTHB, Algiers, Algeria

    Dalila Boughaci

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  1. Dalila Boughaci
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Correspondence to Dalila Boughaci .

Editor information

Editors and Affiliations

  1. University of Portsmouth, Portsmouth, UK

    Max Bramer

  2. Middlesex University, London, UK

    Miltos Petridis

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Boughaci, D. (2019). Stochastic Local Search Based Feature Selection for Intrusion Detection. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXVI. SGAI 2019. Lecture Notes in Computer Science(), vol 11927. Springer, Cham. https://doi.org/10.1007/978-3-030-34885-4_31

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  • DOI: https://doi.org/10.1007/978-3-030-34885-4_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-34884-7

  • Online ISBN: 978-3-030-34885-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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