Intrusion detection system by fuzzy interpolation

Longzhi Yang, Jie Li, Gerhard Fehringer, Phoebe Barraclough, Graham Sexton, Yi Cao

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Citations (Scopus)


Network intrusion detection systems identify malicious connections and thus help protect networks from attacks. Various data-driven approaches have been used in the development of network intrusion detection systems, which usually lead to either very complex systems or poor generalization ability due to the complexity of this challenge. This paper proposes a data-driven network intrusion detection system using fuzzy interpolation in an effort to address the aforementioned limitations. In particular, the developed system equipped with a sparse rule base not only guarantees the online performance of intrusion detection, but also allows the generation of security alerts from situations which are not directly covered by the existing knowledge base. The proposed system has been applied to a well-known data set for system validation and evaluation with competitive results generated.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017
Subtitle of host publication9-12 July 2017, Naples, Italy.
Place of PublicationPiscataway
ISBN (Electronic)9781509060344
ISBN (Print)9781509060351
Publication statusPublished - 24 Aug 2017
Event2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017 - Naples, Italy
Duration: 9 Jul 201712 Jul 2017


Conference2017 IEEE International Conference on Fuzzy Systems, FUZZ 2017


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