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Application of Computational Intelligence in Network Intrusion Detection: A Review

Application of Computational Intelligence in Network Intrusion Detection: A Review

Heba F. Eid
Copyright: © 2018 |Pages: 22
ISBN13: 9781522553960|ISBN10: 1522553967|EISBN13: 9781522553977
DOI: 10.4018/978-1-5225-5396-0.ch008
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MLA

Eid, Heba F. "Application of Computational Intelligence in Network Intrusion Detection: A Review." Handbook of Research on Investigations in Artificial Life Research and Development, edited by Maki Habib, IGI Global, 2018, pp. 153-174. https://doi.org/10.4018/978-1-5225-5396-0.ch008

APA

Eid, H. F. (2018). Application of Computational Intelligence in Network Intrusion Detection: A Review. In M. Habib (Ed.), Handbook of Research on Investigations in Artificial Life Research and Development (pp. 153-174). IGI Global. https://doi.org/10.4018/978-1-5225-5396-0.ch008

Chicago

Eid, Heba F. "Application of Computational Intelligence in Network Intrusion Detection: A Review." In Handbook of Research on Investigations in Artificial Life Research and Development, edited by Maki Habib, 153-174. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5396-0.ch008

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Abstract

Intrusion detection system plays an important role in network security. However, network intrusion detection (NID) suffers from several problems, such as false positives, operational issues in high dimensional data, and the difficulty of detecting unknown threats. Most of the problems with intrusion detection are caused by improper implementation of the network intrusion detection system (NIDS). Over the past few years, computational intelligence (CI) has become an effective area in extending research capabilities. Thus, NIDS based upon CI is currently attracting considerable interest from the research community. The scope of this review will encompass the concept of NID and presents the core methods of CI, including support vector machine, hidden naïve Bayes, particle swarm optimization, genetic algorithm, and fuzzy logic. The findings of this review should provide useful insights into the application of different CI methods for NIDS over the literature, allowing to clearly define existing research challenges and progress, and to highlight promising new research directions.

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