An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations
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-...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2012
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/4665 |
| _version_ | 1848744579894870016 |
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| author | Jiang, F. Ling, S. Chan, Kit Yan Chaczko, Z. Leung, F. Frater, M. |
| author2 | IEEE |
| author_facet | IEEE Jiang, F. Ling, S. Chan, Kit Yan Chaczko, Z. Leung, F. Frater, M. |
| author_sort | Jiang, F. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T06:03:43Z |
| format | Conference Paper |
| id | curtin-20.500.11937-4665 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:03:43Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-46652018-03-29T09:05:23Z An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations Jiang, F. Ling, S. Chan, Kit Yan Chaczko, Z. Leung, F. Frater, M. IEEE Fuzzy logic Immunology Multiple detection engines Hidden Markov model Anomaly intrusion detection 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. 2012 Conference Paper http://hdl.handle.net/20.500.11937/4665 10.1109/FUZZ-IEEE.2012.6251241 IEEE restricted |
| spellingShingle | Fuzzy logic Immunology Multiple detection engines Hidden Markov model Anomaly intrusion detection Jiang, F. Ling, S. Chan, Kit Yan Chaczko, Z. Leung, F. Frater, M. An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations |
| title | An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations |
| title_full | An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations |
| title_fullStr | An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations |
| title_full_unstemmed | An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations |
| title_short | An Immunology-inspired Multi-engine Anomaly Detection System with Hybrid Particle Swarm Optimisations |
| title_sort | immunology-inspired multi-engine anomaly detection system with hybrid particle swarm optimisations |
| topic | Fuzzy logic Immunology Multiple detection engines Hidden Markov model Anomaly intrusion detection |
| url | http://hdl.handle.net/20.500.11937/4665 |