Machine Learning Algorithms for Network Intrusion Detection

Li, Jie, Qu, Yanpeng, Chao, Fei, Shum, Hubert, Ho, Edmond and Yang, Longzhi (2018) Machine Learning Algorithms for Network Intrusion Detection. In: AI in Cybersecurity. Springer, pp. 151-179. ISBN 978-3-319-98841-2

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Official URL: https://doi.org/10.1007/978-3-319-98842-9_6

Abstract

Network intrusion is a growing threat with potentially severe impacts, which can be damaging in multiple ways to network infrastructures and digital/intellectual assets in cyberspace. The approach most commonly employed to combat network intrusion is the development of attack detection systems via machine learning and data mining techniques. These systems can identify and disconnect malicious network traffic, thereby helping to protect networks. This chapter systematically reviews two groups of common intrusion detection systems using fuzzy logic and artificial neural networks, and evaluates them by utilizing the widely used KDD 99 benchmark dataset. Based on the findings, the key challenges and opportunities in addressing cyber-attacks using artificial intelligence techniques are summarized with future work suggested.

Item Type: Book Section
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
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Depositing User: Jie Li
Date Deposited: 06 Dec 2018 14:37
Last Modified: 11 Oct 2019 18:16
URI: http://nrl.northumbria.ac.uk/id/eprint/34481

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