Improved hybrid intelligent intrusion detection system using AI technique
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 of intelligent i...
| Main Authors: | , |
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| Format: | Article |
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Acad. Sciences Czech Republic, Inst. Computer Science
2007
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| Online Access: | http://eprints.utm.my/7128/ |
| _version_ | 1848891408280190976 |
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| author | Shanmugam, Bharanidharan Idris, Norbik Bashah |
| author_facet | Shanmugam, Bharanidharan Idris, Norbik Bashah |
| author_sort | Shanmugam, Bharanidharan |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | 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 of intelligent intrusion detection system, based on a 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 if-then rules that reflect common ways of describing security attacks. We use DARPA dataset for training and benchmarking |
| first_indexed | 2025-11-15T20:57:29Z |
| format | Article |
| id | utm-7128 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T20:57:29Z |
| publishDate | 2007 |
| publisher | Acad. Sciences Czech Republic, Inst. Computer Science |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-71282017-10-22T08:24:35Z http://eprints.utm.my/7128/ Improved hybrid intelligent intrusion detection system using AI technique Shanmugam, Bharanidharan Idris, Norbik Bashah QA75 Electronic computers. Computer science 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 of intelligent intrusion detection system, based on a 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 if-then rules that reflect common ways of describing security attacks. We use DARPA dataset for training and benchmarking Acad. Sciences Czech Republic, Inst. Computer Science 2007 Article PeerReviewed Shanmugam, Bharanidharan and Idris, Norbik Bashah (2007) Improved hybrid intelligent intrusion detection system using AI technique. Neural Network World, 17 (4). pp. 351-362. ISSN 1210-0552 |
| spellingShingle | QA75 Electronic computers. Computer science Shanmugam, Bharanidharan Idris, Norbik Bashah Improved hybrid intelligent intrusion detection system using AI technique |
| title | Improved hybrid intelligent intrusion detection system using AI technique |
| title_full | Improved hybrid intelligent intrusion detection system using AI technique |
| title_fullStr | Improved hybrid intelligent intrusion detection system using AI technique |
| title_full_unstemmed | Improved hybrid intelligent intrusion detection system using AI technique |
| title_short | Improved hybrid intelligent intrusion detection system using AI technique |
| title_sort | improved hybrid intelligent intrusion detection system using ai technique |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/7128/ |