Impact of Feature Reduction on the Efficiency of Wireless Intrusion Detection Systems
 | International Journal of Computer Trends and Technology (IJCTT) |  |
| © July to Aug Issue 2011 by IJCTT Journal | ||
| Volume-1 Issue-3 | ||
| Year of Publication : 2011 | ||
| Authors : Dr. R. Lakshmi Tulasi, M.Ravikanth. |
Dr. R. Lakshmi Tulasi, M.Ravikanth "Impact of Feature Reduction on the Efficiency of Wireless Intrusion Detection Systems"International Journal of Computer Trends and Technology (IJCTT),V1(3):253-258 July to Aug Issue 2011 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.
Abstract: —Intrusion Detection Systems (IDSs) are a major line of defense for protecting network resources from illegal penetrations. A common approach in intrusion detection models, specifically in anomaly detection models, is to use classifiers as detectors. Selecting the best set of features is central to ensuring the performance, speed of learning, accuracy, and reliability of these detectors as well as to remove noise from the set of features used to construct the classifiers. In most current systems, the features used for training and testing the intrusion detection systems consist of basic information related to the TCP/IP header, with no considerable attention to the features associated with lower level protocol frames. The resulting detectors were efficient and accurate in detecting network attacks at the network and transport layers, but unfortunately, not capable of detecting 802.11-specific attacks such as deauthentication attacks or MAC layer DoS attacks.
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KeywordsFeature selection, intrusion detection systems, K-means, information gain ratio, wireless networks, neural networks