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.
 A. Boukerche, R.B. Machado, K.R.L. Juca´ , J.B.M. Sobral, and M.S.M.A. Notare, “An Agent Based and Biological Inspired Real- Time Intrusion Detection and Security Model for Computer Network Operations,” Computer Comm., vol. 30, no. 13, pp. 2649- 2660, Sept. 2007.
 A. Boukerche, K.R.L. Juc, J.B. Sobral, and M.S.M.A. Notare, “An Artificial Immune Based Intrusion Detection Model for Computer and Telecommunication Systems,” Parallel Computing, vol. 30, nos. 5/6, pp. 629-646, 2004.
 A. Boukerche and M.S.M.A. Notare, “Behavior-Based Intrusion Detection in Mobile Phone Systems,” J. Parallel and Distributed Computing, vol. 62, no. 9, pp. 1476-1490, 2002.
 Y. Chen, Y. Li, X. Cheng, and L. Guo, “Survey and Taxonomy of Feature Selection Algorithms in Intrusion Detection System,” Proc. Conf. Information Security and Cryptology (Inscrypt), 2006.
 H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic, 1998.
 http://kdd.ics.uci.edu/databases/kddcup99/task.html, 2010.
 A.H. Sung and S. Mukkamala, “The Feature Selection and Intrusion Detection Problems,” Proc. Ninth Asian Computing Science Conf., 2004.
 A.H. Sung and S. Mukkamala, “Identifying Important Features for Intrusion Detection Using Support Vector Machines and Neural Networks,” Proc. Symp. Applications and the Internet (SAINT ’03), Jan. 2003.
 G. Stein, B. Chen, A.S. Wu, and K.A. Hua, “Decision Tree Classifier for Network Intrusion Detection with GA-Based Feature Selection,” Proc. 43rd ACM Southeast Regional Conf.—Volume 2, Mar. 2005.
 A. Hofmann, T. Horeis, and B. Sick, “Feature Selection for Intrusion Detection: An Evolutionary Wrapper Approach,” Proc. IEEE Int’l Joint Conf. Neural Networks, July 2004.
 J. Bellardo and S. Savage, “802.11 Denial-of-Service Attacks: Real Vulnerabilities and Practical Solutions,” Proc. USENIX Security Symp., pp. 15-28, 2003.
 http://www.aircrack-ng.org/, 2010.
 Y.-H. Liu, D.-X. Tian, and D. Wei, “A Wireless Intrusion Detection Method Based on Neural Network,” Proc. Second IASTED Int’l Conf. Advances in Computer Science and Technology, Jan. 2006.
 T.M. Khoshgoftaar, S.V. Nath, S. Zhong, and N. Seliya, “Intrusion Detection inWireless Networks Using Clustering Techniques with Expert Analysis,” Proc. Fourth Int’l Conf. Machine Learning and Applications, Dec. 2005.
 S. Zhong, T.M. Khoshgoftaar, and S.V. Nath, “A Clustering Approach to Wireless Network Intrusion Detection,” Proc. 17th IEEE Int’l Conf. Tools with Artificial Intelligence (ICTAI ’05), Nov. 2005.
KeywordsFeature selection, intrusion detection systems, K-means, information gain ratio, wireless networks, neural networks