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Attribute Reduction based Hybrid Anomaly Intrusion Detection using K-Means and SVM Classifier
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 82 - Number 15 |
| Year of Publication: 2013 |
| Authors: Ujwala Ravale, Nilesh Marathe, Puja Padiya |
10.5120/14242-2448
|
Ujwala Ravale, Nilesh Marathe, Puja Padiya . Attribute Reduction based Hybrid Anomaly Intrusion Detection using K-Means and SVM Classifier. International Journal of Computer Applications. 82, 15 ( November 2013), 32-35. DOI=10.5120/14242-2448
Abstract
In Information Security, intrusion detection is the act of detecting actions that attempt to compromise the confidentiality, integrity or availability of a resource. One of the primary challenges to intrusion detection is the problem of misjudgment, misdetection and lack of real time response to the attack. Various data mining techniques such as clustering, classification and association rule discovery are being used for intrusion detection. The proposed hybrid technique combines data mining approaches like K Means clustering algorithm and Support Vector Machine classification module. The main purpose of proposed technique is to decrease the number of attributes associated with each data point. So that, the proposed technique can perform better in terms of Detection Rate and Accuracy when applied to KDD’99 Data Set.
References
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