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A Machine Learning Based Network Intrusion Detection System with Applying Different Algorithm in Multiple Stages

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Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 346)

Abstract

In recent years, a wide variety of network services are provided by the rapid development of Information and Communication Technology (ICT). Along with this, cyberattacks which interfere with these services occur frequently and the damage is increasing. Therefore, there is a need to strengthen countermeasures against cyberattacks and to minimize damage by responding quickly and with high accuracy. Therefore, in order to enhance security measures in network environments, a lot of research has been conducted to improve the performance of intrusion detection systems by applying machine learning to them. However, there are many false positives, and machine learning is not yet able to classify and detect them completely. In this study, we aimed to reduce the number of false positives by applying different machine learning algorithms in multiple stages to the intrusion detection system.

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Correspondence to Seiichi Sasa .

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Sasa, S., Suzuki, H., Koyama, A. (2022). A Machine Learning Based Network Intrusion Detection System with Applying Different Algorithm in Multiple Stages. In: Barolli, L. (eds) Advances on Broad-Band Wireless Computing, Communication and Applications. BWCCA 2021. Lecture Notes in Networks and Systems, vol 346. Springer, Cham. https://doi.org/10.1007/978-3-030-90072-4_10

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  • DOI: https://doi.org/10.1007/978-3-030-90072-4_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90071-7

  • Online ISBN: 978-3-030-90072-4

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