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dc.contributor.authorJiang, F.
dc.contributor.authorLing, S.
dc.contributor.authorChan, Kit Yan
dc.contributor.authorChaczko, Z.
dc.contributor.authorLeung, F.
dc.contributor.authorFrater, M.
dc.contributor.editorIEEE
dc.date.accessioned2017-01-30T10:40:51Z
dc.date.available2017-01-30T10:40:51Z
dc.date.created2012-06-18T20:00:49Z
dc.date.issued2012
dc.date.submitted2012-11-01
dc.identifier.citationJiang, Frank and Ling, Sai Ho and Chan, Kit Yan and Chaczko, Zenon and Leung, Frank and Frater, Michael. 2012. An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations, in IEEE International Conference on Fuzzy Systems, Jun 10-15 2012, pp. 1279-1286. Sydney, NSW: IEEE.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/4665
dc.identifier.doi10.1109/FUZZ-IEEE.2012.6251241
dc.description.abstract

In this paper, multiple detection engines with multilayered intrusion detection mechanisms are proposed for enhancing computer security. The principle is to coordinate the results from each single-engine intrusion alert system, which seamlessly integrates with a multiple layered distributed service-oriented structure. An improved hidden Markov model (HMM) is created for the detection engine which is capable of the immunology based self/nonself discrimination. The classifications of normal and abnormal behaviours of system calls are further examined by an advanced fuzzy-based inference process tuned by HPSOWM. Considering a real benchmark dataset from the public domain, our experimental results show that the proposed scheme can greatly shorten the training time of HMM and significantly reduce the false positive rate. The proposed HPSOWM works especially well for the efficient classification of unknown behaviors and malicious attacks.

dc.publisherIEEE
dc.subjectFuzzy logic
dc.subjectImmunology
dc.subjectMultiple detection engines
dc.subjectHidden Markov model
dc.subjectAnomaly intrusion detection
dc.titleAn Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations
dc.typeConference Paper
dcterms.dateSubmitted2012-06-19
dcterms.source.startPage1279
dcterms.source.endPage1286
dcterms.source.titleProceedings of the IEEE International Conference on Fuzzy Systems
dcterms.source.seriesProceedings of the IEEE International Conference on Fuzzy Systems
dcterms.source.isbn978-1-4673-1505-0
dcterms.source.conferenceIEEE International Conference on Fuzzy Systems
dcterms.source.conferencedatesJun 10 2012
dcterms.source.conferencelocationAustralia
dcterms.source.placeUSA
curtin.digitool.pid186258
curtin.department
curtin.accessStatusFulltext not available


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