Integrating genetic algorithms and fuzzy c-means for anomaly detection
The goal of intrusion detection is to discover unauthorized use of computer systems. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and ex...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2005
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| Subjects: | |
| Online Access: | http://eprints.utm.my/7451/ http://eprints.utm.my/7451/1/Abdullah_Abd_Hanan_2005_Integrating_Genetic_Algorithms_Fuzzy_c-Means.pdf |
| _version_ | 1848891473133568000 |
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| author | Chimphlee, Witcha Abdullah, Abdul Hanan Sap, Noor Md. Chimphlee, Siriporn Srinoy, Surat |
| author_facet | Chimphlee, Witcha Abdullah, Abdul Hanan Sap, Noor Md. Chimphlee, Siriporn Srinoy, Surat |
| author_sort | Chimphlee, Witcha |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | The goal of intrusion detection is to discover unauthorized use of computer systems. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. In this paper we propose an intrusion detection method that combines Fuzzy Clustering and Genetic Algorithms. Clustering-based intrusion detection algorithm which trains on unlabeled data in order to detect new intrusions. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Genetic Algorithms (GA) to the problem of selection of optimized feature subsets to reduce the error caused by using land-selected features. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate. We used data set from 1999 KDD intrusion detection contest. |
| first_indexed | 2025-11-15T20:58:31Z |
| format | Conference or Workshop Item |
| id | utm-7451 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:58:31Z |
| publishDate | 2005 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-74512017-08-28T08:36:09Z http://eprints.utm.my/7451/ Integrating genetic algorithms and fuzzy c-means for anomaly detection Chimphlee, Witcha Abdullah, Abdul Hanan Sap, Noor Md. Chimphlee, Siriporn Srinoy, Surat QA75 Electronic computers. Computer science The goal of intrusion detection is to discover unauthorized use of computer systems. New intrusion types, of which detection systems are unaware, are the most difficult to detect. The amount of available network audit data instances is usually large; human labeling is tedious, time-consuming, and expensive. Traditional anomaly detection algorithms require a set of purely normal data from which they train their model. In this paper we propose an intrusion detection method that combines Fuzzy Clustering and Genetic Algorithms. Clustering-based intrusion detection algorithm which trains on unlabeled data in order to detect new intrusions. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Genetic Algorithms (GA) to the problem of selection of optimized feature subsets to reduce the error caused by using land-selected features. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate. We used data set from 1999 KDD intrusion detection contest. 2005 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/7451/1/Abdullah_Abd_Hanan_2005_Integrating_Genetic_Algorithms_Fuzzy_c-Means.pdf Chimphlee, Witcha and Abdullah, Abdul Hanan and Sap, Noor Md. and Chimphlee, Siriporn and Srinoy, Surat (2005) Integrating genetic algorithms and fuzzy c-means for anomaly detection. In: Proceedings of INDICON 2005: An International Conference of IEEE India Council . http://dx.doi.org/10.1109/INDCON.2005.1590237 |
| spellingShingle | QA75 Electronic computers. Computer science Chimphlee, Witcha Abdullah, Abdul Hanan Sap, Noor Md. Chimphlee, Siriporn Srinoy, Surat Integrating genetic algorithms and fuzzy c-means for anomaly detection |
| title | Integrating genetic algorithms and fuzzy c-means for anomaly detection |
| title_full | Integrating genetic algorithms and fuzzy c-means for anomaly detection |
| title_fullStr | Integrating genetic algorithms and fuzzy c-means for anomaly detection |
| title_full_unstemmed | Integrating genetic algorithms and fuzzy c-means for anomaly detection |
| title_short | Integrating genetic algorithms and fuzzy c-means for anomaly detection |
| title_sort | integrating genetic algorithms and fuzzy c-means for anomaly detection |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/7451/ http://eprints.utm.my/7451/ http://eprints.utm.my/7451/1/Abdullah_Abd_Hanan_2005_Integrating_Genetic_Algorithms_Fuzzy_c-Means.pdf |