Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means
As malicious intrusions are a growing problem, we need a solution to detect the intrusions accurately. Network administrators are continuously looking for new ways to protect their resources from harm, both internally and externally. Intrusion detection systems look for unusual or suspicious activit...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UTM Press
2005
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| Online Access: | http://eprints.utm.my/8475/ http://eprints.utm.my/8475/1/MohdNoorMdSap2005_AnomalyDetectionOfInstrusionBasedOn.PDF |
| _version_ | 1848891695164293120 |
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| author | Chimphlee, Witcha Md. Sap, Mohd. Noor Abdullah, Abdul Hanan Chimphlee, Siriporn |
| author_facet | Chimphlee, Witcha Md. Sap, Mohd. Noor Abdullah, Abdul Hanan Chimphlee, Siriporn |
| author_sort | Chimphlee, Witcha |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | As malicious intrusions are a growing problem, we need a solution to detect the intrusions accurately. Network administrators are continuously looking for new ways to protect their resources from harm, both internally and externally. Intrusion detection systems look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. 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. The objective of this paper is to describe a rough sets and fuzzy c-means algorithms and discuss its usage to detect intrusion in a computer network. Fuzzy systems have demonstrated their ability to solve different kinds of problems in various applications domains. We are using a Rough Sets to select a subset of input features for clustering with a goal of increasing the detection rate and decreasing the false alarm rate in network intrusion detection. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Experiments were performed with DARPA data sets, which have information on computer networks, during normal behavior and intrusive behavior. |
| first_indexed | 2025-11-15T21:02:03Z |
| format | Article |
| id | utm-8475 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T21:02:03Z |
| publishDate | 2005 |
| publisher | Penerbit UTM Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-84752017-11-01T04:17:31Z http://eprints.utm.my/8475/ Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means Chimphlee, Witcha Md. Sap, Mohd. Noor Abdullah, Abdul Hanan Chimphlee, Siriporn QA75 Electronic computers. Computer science As malicious intrusions are a growing problem, we need a solution to detect the intrusions accurately. Network administrators are continuously looking for new ways to protect their resources from harm, both internally and externally. Intrusion detection systems look for unusual or suspicious activity, such as patterns of network traffic that are likely indicators of unauthorized activity. 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. The objective of this paper is to describe a rough sets and fuzzy c-means algorithms and discuss its usage to detect intrusion in a computer network. Fuzzy systems have demonstrated their ability to solve different kinds of problems in various applications domains. We are using a Rough Sets to select a subset of input features for clustering with a goal of increasing the detection rate and decreasing the false alarm rate in network intrusion detection. Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. Experiments were performed with DARPA data sets, which have information on computer networks, during normal behavior and intrusive behavior. Penerbit UTM Press 2005-12 Article PeerReviewed application/pdf en http://eprints.utm.my/8475/1/MohdNoorMdSap2005_AnomalyDetectionOfInstrusionBasedOn.PDF Chimphlee, Witcha and Md. Sap, Mohd. Noor and Abdullah, Abdul Hanan and Chimphlee, Siriporn (2005) Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means. Jurnal Teknologi Maklumat, 17 (2). pp. 1-14. ISSN 0128-3790 https://core.ac.uk/display/11784316 |
| spellingShingle | QA75 Electronic computers. Computer science Chimphlee, Witcha Md. Sap, Mohd. Noor Abdullah, Abdul Hanan Chimphlee, Siriporn Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means |
| title | Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means |
| title_full | Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means |
| title_fullStr | Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means |
| title_full_unstemmed | Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means |
| title_short | Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means |
| title_sort | anomaly detection of intrusion based on integration of rough sets and fuzzy c-means |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/8475/ http://eprints.utm.my/8475/ http://eprints.utm.my/8475/1/MohdNoorMdSap2005_AnomalyDetectionOfInstrusionBasedOn.PDF |