Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/11178
Title: An immunology-inspired multi-engine anomaly detection system with hybrid particle swarm optimisations
Authors: Jiang, F
Ling, SH
Chan, KY
Chaczko, Z
Leung, FHF 
Frater, MR
Issue Date: 2012
Source: 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 10-15 June 2012, Brisbane, QLD, p. 1-8
Abstract: In this paper, multiple detection engines with multi-layered 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.
Keywords: Anomaly intrusion detection
Fuzzy logic
Hidden Markov model
Immunology
Multiple detection engines
Publisher: IEEE
ISBN: 978-1-4673-1507-4
978-1-4673-1505-0 (E-ISBN)
ISSN: 1098-7584
DOI: 10.1109/FUZZ-IEEE.2012.6251241
Appears in Collections:Conference Paper

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