Impact of feature reduction on the efficiency of wireless intrusion detection systems

IEEE Transactions on Parallel and Distributed Systems

Volumn 21, Issue 8, 2010, Pages 1143-1149

  El Khatib, Khalil ^a  

^a UNIVERSITY OF ONTARIO INSTITUTE OF TECHNOLOGY   (Canada)

Author keywords

Feature selection; information gain ratio; intrusion detection systems; K means; neural networks; wireless networks

Indexed keywords

ANOMALY DETECTION MODELS; CURRENT SYSTEM; DOS ATTACKS; EXPERIMENTAL SECTION; FEATURE REDUCTION; FEATURE SELECTION; FEATURE SETS; HYBRID MODEL; INFORMATION GAIN RATIO; INTRUSION DETECTION MODELS; INTRUSION DETECTION SYSTEMS; K-MEANS; LEARNING TIME; MAC LAYER; NETWORK ATTACK; NETWORK RESOURCE; OPTIMAL SETS; REMOVE NOISE; SIGNIFICANT IMPACTS; TCP/IP HEADERS; TRAINING AND TESTING; TRANSPORT LAYERS; WIRELESS INTRUSION;

CLASSIFIERS; COMPUTER CRIME; DETECTORS; FEATURE EXTRACTION; LEARNING ALGORITHMS; NEURAL NETWORKS; OPTIMIZATION; WIRELESS NETWORKS;

INTRUSION DETECTION;

EID: 77954310491     PISSN: 10459219     EISSN: None     Source Type: Journal    
DOI: 10.1109/TPDS.2009.142     Document Type: Article

References (26)

1. A. Boukerche, R.B. Machado, K.R.L. Jucá , 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.
2. 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.
3. 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.
4. 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.
5. H. Liu and H. Motoda, Feature Selection for Knowledge Discovery and Data Mining. Kluwer Academic, 1998.
6. http://kdd.ics.uci.edu/databases/kddcup99/task.html, 2010.
7. A.H. Sung and S. Mukkamala, "The Feature Selection and Intrusion Detection Problems," Proc. Ninth Asian Computing Science Conf., 2004.
8. 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.
9. 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.
10. 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.
11. J. Bellardo and S. Savage, "802.11 Denial-of-Service Attacks: Real Vulnerabilities and Practical Solutions," Proc. USENIX Security Symp., pp. 15-28, 2003.
12. http://www.aircrack-ng.org/, 2010.
13. 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.
14. 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.
15. 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.
16. A. Bittau, M. Handley, and J. Lackey, "The Final Nail in WEP's Coffin," Proc. IEEE Symp. Security and Privacy, May 2006.
17. IEEE 802.11-1999, Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications: Higher Speed Physical Layer Extension in the 2.4 GHz Band.
18. J.R. Quinlan, "Induction of Decision Trees," Machine Learning, vol.1, pp. 81-106, 1986.
19. Z. Zhang and C. Manikopoulos, "Investigation of Neural Network Classification of Computer Network Attacks," Proc. Int'l Conf. Information Technology: Research and Education, pp. 590-594, Aug. 2003.
20. NeuroSolutions, Inc., http://www.neurosolutions.com/, 2010.
21. CERT, http://www.cert.org/stats/, 2010.
22. J. Yeo, M. Youssef, and A. Agrawala, "A Framework for Wireless LAN Monitoring and Its Applications," Proc. ACM Workshop Wireless Security, pp. 70-79, 2004.
23. 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.
24. A. Boukerche, Algorithms and Protocols for Wireless and Mobile Ad Hoc Networks. Wiley, 2008.
25. A. Boukerche, Handbook of Algorithms for Wireless Networking and Mobile Computing. CRC/Chapman and Hall, 2005.
26. A. Boukerche, X. Cheng, and J. Linus, "Energy-Aware Data- Centric Routing in Microsensor Networks," Proc. Sixth ACM Int'l Workshop Modeling Analysis and Simulation of Wireless and Mobile Systems, pp. 42-49, 2003.