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 language | English |
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Title of host publication | 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2018) |
Publisher | IEEE |
Number of pages | 7 |
ISBN (Electronic) | 9781509060207 |
ISBN (Print) | 9781509060214 |
DOIs | |
Publication status | Published - 15 Oct 2018 |
Event | IEEE International Conference on Fuzzy Systems 2018 - Rio de Janeiro, Brazil Duration: 8 Jul 2018 → 13 Jul 2018 http://www.ecomp.poli.br/~wcci2018/ (Link to Conference Website) |
Conference
Conference | IEEE International Conference on Fuzzy Systems 2018 |
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Abbreviated title | FUZZ-IEEE 2018 |
Country/Territory | Brazil |
City | Rio de Janeiro |
Period | 8/07/18 → 13/07/18 |
Internet address |
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