Unsupervised Anomaly Detection with Unlabeled Data Using Clustering
Intrusions pose a serious security risk in a network environment. 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. Traditiona...
| 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/3356/ http://eprints.utm.my/3356/1/Mohd_Noor_-_Unsupervised_Anomaly_Detection_with_Unlabeled_Data.pdf |
| _version_ | 1848890553807142912 |
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| author | Chimphlee, Witcha Abdullah, Abdul Hanan Md. Sap, Mohd. Noor |
| author_facet | Chimphlee, Witcha Abdullah, Abdul Hanan Md. Sap, Mohd. Noor |
| author_sort | Chimphlee, Witcha |
| building | UTeM Institutional Repository |
| collection | Online Access |
| description | Intrusions pose a serious security risk in a network environment. 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. We present a clustering-based intrusion detection algorithm, unsupervised anomaly detection, which trains on unlabeled data in order to detect new intrusions. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate as verified over the Knowledge Discovery and Data Mining - KDD CUP 1999 dataset. |
| first_indexed | 2025-11-15T20:43:54Z |
| format | Conference or Workshop Item |
| id | utm-3356 |
| institution | Universiti Teknologi Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T20:43:54Z |
| publishDate | 2005 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | utm-33562017-08-30T07:28:39Z http://eprints.utm.my/3356/ Unsupervised Anomaly Detection with Unlabeled Data Using Clustering Chimphlee, Witcha Abdullah, Abdul Hanan Md. Sap, Mohd. Noor QA75 Electronic computers. Computer science Intrusions pose a serious security risk in a network environment. 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. We present a clustering-based intrusion detection algorithm, unsupervised anomaly detection, which trains on unlabeled data in order to detect new intrusions. Our method is able to detect many different types of intrusions, while maintaining a low false positive rate as verified over the Knowledge Discovery and Data Mining - KDD CUP 1999 dataset. 2005-05-17 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/3356/1/Mohd_Noor_-_Unsupervised_Anomaly_Detection_with_Unlabeled_Data.pdf Chimphlee, Witcha and Abdullah, Abdul Hanan and Md. Sap, Mohd. Noor (2005) Unsupervised Anomaly Detection with Unlabeled Data Using Clustering. In: Postgraduate Annual Research Seminar 2005, May 2005. |
| spellingShingle | QA75 Electronic computers. Computer science Chimphlee, Witcha Abdullah, Abdul Hanan Md. Sap, Mohd. Noor Unsupervised Anomaly Detection with Unlabeled Data Using Clustering |
| title | Unsupervised Anomaly Detection with Unlabeled Data Using Clustering |
| title_full | Unsupervised Anomaly Detection with Unlabeled Data Using Clustering |
| title_fullStr | Unsupervised Anomaly Detection with Unlabeled Data Using Clustering |
| title_full_unstemmed | Unsupervised Anomaly Detection with Unlabeled Data Using Clustering |
| title_short | Unsupervised Anomaly Detection with Unlabeled Data Using Clustering |
| title_sort | unsupervised anomaly detection with unlabeled data using clustering |
| topic | QA75 Electronic computers. Computer science |
| url | http://eprints.utm.my/3356/ http://eprints.utm.my/3356/1/Mohd_Noor_-_Unsupervised_Anomaly_Detection_with_Unlabeled_Data.pdf |