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• W2030058741 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." @default.
• W2030058741 created "2016-06-24" @default.
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• W2030058741 date "2012-06-01" @default.
• W2030058741 modified "2023-09-24" @default.
• W2030058741 title "An immunology-inspired multi-engine anomaly detection system with hybrid particle swarm optimisations" @default.
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• W2030058741 doi "https://doi.org/10.1109/fuzz-ieee.2012.6251241" @default.
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