Weighted Fuzzy Rules Optimised by Particle Swarm for Network Intrusion Detection

Tianhua Chen, Pan Su, Changjing Shang, Qiang Shen

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

8 Dyfyniadau(SciVal)
170 Wedi eu Llwytho i Lawr (Pure)


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.
Iaith wreiddiolSaesneg
Teitl2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
CyhoeddwrIEEE Press
Nifer y tudalennau7
ISBN (Electronig)978-1-5090-6020-7
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 14 Hyd 2018
DigwyddiadFuzzy Systems - Rio de Janeiro, Brasil
Hyd: 08 Gorff 201813 Gorff 2018
Rhif y gynhadledd: 27

Cyfres gyhoeddiadau

EnwInternational Conference on Fuzzy Systems


CynhadleddFuzzy Systems
Teitl crynoFUZZ-IEEE-2018
DinasRio de Janeiro
Cyfnod08 Gorff 201813 Gorff 2018

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