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Siamese Network Based Feature Learning for Improved Intrusion Detection

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Book cover Neural Information Processing (ICONIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11953))

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Abstract

Intrusion detection is a critical Cyber Security subject. Different Machine Learning (ML) approaches have been proposed for Intrusion Detection Systems (IDS). However, their application to real-life scenarios remains challenging due to high data dimensionality. Representation learning (RL) allows discriminative feature representation in a low dimensionality space. The application of this technique in IDS requires more investigation. This paper examines and discusses the contribution of Siamese network based representation learning in improving the IDS performance. Extensive experimental results under different evaluation scenarios show different improvement rates depending on the scenario.

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Author information

Authors and Affiliations

  1. Samovar, CNRS, Télécom SudParis, Institut Polytechnique de Paris, 9 rue Charles Fourier, 91011, Evry Cedex, France

    Houda Jmila, Gregory Blanc & Mounim A. El Yacoubi

  2. IRT - SystemX, 8 Avenue de la Vauve, 91120, Palaiseau, France

    Mohamed Ibn Khedher

Authors
  1. Houda Jmila
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  2. Mohamed Ibn Khedher
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  3. Gregory Blanc
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  4. Mounim A. El Yacoubi
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Corresponding author

Correspondence to Houda Jmila .

Editor information

Editors and Affiliations

  1. Australian National University, Canberra, ACT, Australia

    Tom Gedeon

  2. Murdoch University, Murdoch, WA, Australia

    Kok Wai Wong

  3. Kyungpook National University, Daegu, Korea (Republic of)

    Minho Lee

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Jmila, H., Ibn Khedher, M., Blanc, G., El Yacoubi, M.A. (2019). Siamese Network Based Feature Learning for Improved Intrusion Detection. In: Gedeon, T., Wong, K., Lee, M. (eds) Neural Information Processing. ICONIP 2019. Lecture Notes in Computer Science(), vol 11953. Springer, Cham. https://doi.org/10.1007/978-3-030-36708-4_31

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  • DOI: https://doi.org/10.1007/978-3-030-36708-4_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36707-7

  • Online ISBN: 978-3-030-36708-4

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