Weighted Fuzzy Rules Optimised by Particle Swarm for Network Intrusion Detection

Tianhua Chen, Pan Su, Changjing Shang, Qiang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

Network intrusion detection systems (IDSs) dynamically monitor communication events on a network, and decide whether any event is symptomatic of an attack or constitutes a legitimate use of the system. They have become an indispensable component of security infrastructure, e.g., to detect threats before widespread damage takes place. A variety of approaches have been proposed to design IDSs, including fuzzy rule-based techniques that offer advantages such as tolerance of noisy and imprecise data. In particular, fuzzy rules can be highly interpretable and trackable if the underlying fuzzy sets are predefined, directly reflecting domain expertise. This paper proposes such an approach to generate a set of weighted fuzzy rules for building effective IDSs, where the rule weights are optimised by Particle Swarm Optimisation without affecting the underlying predefined fuzzy sets. Experiments are performed on benchmark IDS datasets with comparison to alternative systems built with popular machine learning methods.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018)
PublisherIEEE
Number of pages7
ISBN (Electronic)9781509060207
ISBN (Print)9781509060214
DOIs
Publication statusPublished - 15 Oct 2018
EventIEEE International Conference on Fuzzy Systems - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018
http://www.ecomp.poli.br/~wcci2018/ (Link to Conference Website)

Conference

ConferenceIEEE International Conference on Fuzzy Systems
Abbreviated titleFUZZ-IEEE 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18
Internet address

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Cite this

Chen, T., Su, P., Shang, C., & Shen, Q. (2018). Weighted Fuzzy Rules Optimised by Particle Swarm for Network Intrusion Detection. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018) [18166140] IEEE. https://doi.org/10.1109/FUZZ-IEEE.2018.8491553