Proceedings Article10.1109/ICMLC.2004.1382006
A supervised intrusion detection method
Qing-Hua Li,Sheng-Yi Jiang,Xin Li +2 more
- 26 Aug 2004
- Vol. 3, pp 1475-1479
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TL;DR: A supervised intrusion detection method with new distance definition based on constrained clustering that can detect unknown intrusions and has promising performance with high detection rate and low false alarm rate is proposed.
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Abstract: A supervised intrusion detection method with new distance definition is proposed in this paper. This method based on constrained clustering, uses the produced clusters as classification model to predict which cluster the current data belongs to. The time complexity of the method is nearly linear with the size of dataset, the number of attributes and the final number of clusters. It is difference from existing supervised methods that our method can detect unknown intrusions. The experiment results on dataset KDDCUP99 demonstrate that the method has promising performance with high detection rate and low false alarm rate.
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Citations
Applying fuzzy data mining to network unsupervised anomaly detection
Gao Xiang,Wang Min,Zhao Rongchun +2 more
- 12 Oct 2005
TL;DR: The creation of a system that uses a fuzzy cluster algorithm to detect anomalies in network connections that has the added versatility of being free of the over specialization that comes with systems tailored for specific sets of attacks is discussed.
5
Application of Fuzzy ART for Unsupervised Anomaly Detection System
Gao Xiang,Wang Min,Zhao Rongchun +2 more
- 01 Nov 2006
TL;DR: This paper discusses the creation of a system that uses fuzzy ART to detect anomalies in network connections; the method is evaluated by performing experiments over network records from the KDD CUP99 data set.
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References
Intrusion detection with unlabeled data using clustering
Leonid Portnoy
- 01 Jan 2000
TL;DR: “¦e4&2¦2nn¤2 U ¥ Se¦§¯4e ̈©SS ‘¬’¦ e-S«S«
A Geometric Framework for Unsupervised Anomaly Detection
Eleazar Eskin,Andrew Arnold,Michael J. Prerau,Leonid Portnoy,Salvatore J. Stolfo +4 more
- 01 Jan 2002
TL;DR: A new geometric framework for unsupervised anomaly detection is presented, which are algorithms that are designed to process unlabeled data to detect anomalies in sparse regions of the feature space.
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Mining in a data-flow environment: experience in network intrusion detection
Wenke Lee,Salvatore J. Stolfo,Kui W. Mok +2 more
- 01 Aug 1999
TL;DR: It is shown that in order to minimize the time required in using the classification models in a real-time environment, the “necessary conditions” associated with the lowcost features can be exploited to determine whether some high-cost features need to be computed and the corresponding classification rules need to been checked.