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Siamese Network Based Feature Learning for Improved Intrusion Detection

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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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Correspondence to Houda Jmila .

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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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