Hybrid intelligent intrusion detection/prevention system using fuzzylogic and data mining

B. Shanmugam, N.B. Idris

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

Abstract

Intrusion Detection Systems are increasingly a key part of systems defense. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Artificial Intelligence plays a driving role in security services. This paper proposes a dynamic model Intelligent Intrusion Detection System, based on specific AI approach for intrusion detection. The techniques that are being investigated include fuzzy logic with network profiling, which uses simple data mining techniques to process the network data. The proposed hybrid system combines anomaly and misuse detection. Simple fuzzy rules, allow us to construct ifthen rules that reflect common ways of describing security attacks. We use DARPA dataset for training and benchmarking.
Original languageEnglish
Title of host publicationProceedings of the 6th European Conference on Information Warfare and Security 2007, ECIW 2007
Pages237-244
Number of pages8
Publication statusPublished - 2007
Externally publishedYes
Event6th European Conference on Information Warfare and Security 2007 - Shrivenham; United Kingdom
Duration: 2 Jul 20073 Jul 2007
Conference number: 95213

Conference

Conference6th European Conference on Information Warfare and Security 2007
Abbreviated titleECIW 2007
Period2/07/073/07/07

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Shanmugam, B., & Idris, N. B. (2007). Hybrid intelligent intrusion detection/prevention system using fuzzylogic and data mining. In Proceedings of the 6th European Conference on Information Warfare and Security 2007, ECIW 2007 (pp. 237-244)