Role of Feature Reduction in Intrusion Detection Systems for Wireless Attacks
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2013 by IJETT Journal|
|Year of Publication : 2013|
|Authors : Jashuva.Chundi , V.V.Gopala Rao|
Jashuva.Chundi , V.V.Gopala Rao. "Role of Feature Reduction in Intrusion Detection Systems for Wireless Attacks". International Journal of Engineering Trends and Technology (IJETT). V6(5):241-246 Dec 2013. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Although of the widespread use of the WLANs, it is still vulnerable for the availability security issues. This research presents a proposal Wireless Network Intrusion Detection System (WNIDS) which is use misuse and anomaly techniques in intrusion detection. The proposal depend on Data mining is a DM-based WNIDS since mining provide iterative process so if results are not satisfied with optimal solution, the mining steps will continue to be carried out until mining results are corresponding intention results. For training and testing of WNIDS in our experiment, we used collected dataset called it W data set, the collection done on an organized WLAN 802.11 consist of 5 machines. The collection of data involved frames from all types (normal and the four known intrusions and unknown intrusion).The collected connections contain features those appear directly in the header of 802.11 frames and we added one more feature (casting) since it is critical in distinguish among intrusions. These connections are labelled as either normal or attack type, many of these features are irrelative in classification process. Here we propose Support Vector Machine SVM classifier as feature extraction to reduce no. of features to avoid time consuming in training and real-time detecting. SVM introduce 8 features as subset of correlated intrinsic features present the basic point in classification. The sets of features that have been resulted from SVM and the all features set will be the feeding of WNIDS. The results obtained from WNIDS showing that accuracy rate of ANN and ID3 classifiers are both higher with SVM (8) features than set of all features. And absolutely, ANN accuracy is higher than ID3 with both sets of features.
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Feature selection, intrusion detection systems, K-means, information gain ratio, wireless networks, neural networks.