..............................
..............................
..............................
A Cross-Layer Based Intrusion Detection Technique for Wireless Networks
In this paper, we propose to design a cross-layer b ased intrusion detection technique for wireless networks. In this
technique a combined weight value is computed from the Received Signal Strength (RSS) and Time Taken for RTS-CTS
handshake between sender and receiver (TT). Since i t is not possible for an attacker to assume the RSS exactly for a sender by
a receiver, it is an useful measure for intrusion d etection. We propose that we can develop a dynamic profile for the
communicating nodes based on their RSS values throu gh monitoring the RSS values periodically for a specific Mobile Station
(MS) or a Base Station (BS) from a server. Monitori ng observed TT
values at the server provides a reliable passive detection
mechanism for session hijacking attacks since it is an unspoofable parameter related to its measuring entity. If the weight
value is greater than a threshold value, then the c orresponding node is considered as an attacker. By suitably adjusting the
threshold value and the weight constants, we can re duce the false positive rate, significantly. By simulation results, we show
that our proposed technique attains low misdetectio n ratio and false positive rate while increasing the packet delivery ratio.
[1] Carver C., Humphries J., and Pooch U., Adaptation Techniques for Intrusion Detection and Intrusion Response Systems, in Proceedings of IEEE International Conference on Systems Man and Cybernetics , USA, pp. 2344-2349, 2000 .
[2] Mosqueira-Rey E., Alonso-Betanzos A., Rio B., and Pineiro J., A Misuse Detection Agent for A Cross-Layer Based Intrusion Detection Technique for Wireless Networks 207 Intrusion Detection in a Multi-agent Architecture, in Proceedings of the 1st KES International Symposium on Agent and Multi- Agent Systems: Technologies and Applications , Berlin, pp. 466-475, 2007.
[3] Bellaaj H., Ketata R., and Hsini A., Fuzzy Approach for 802.11 Wireless Intrusion Detection, in Proceedings of 4 th International Conference: Sciences of Electronic, Technologies of Information and Telecommunications , Tunisia, pp. 1-7, 2007.
[4] Debar H., An Introduction to Intrusion- Detection Systems, in Proceedings of Connect, IBM Research, USA, pp. 1-18, 2002.
[5] Barbeau J. and Kranakis E., Enhancing Intrusion Detection in Wireless Networks Using Radio Frequency Fingerprinting, in Proceedings of the 3 rd IASTED International Conference on Communications, Internet and Information Technology (CIIT} , Kranakis, pp. 201-206, 2004.
[6] Salmanian M., Lefebvre J., Leonard S., and Knight S., Intrusion Detection in 802.11 Wireless Local Area Networks , Defence R&D Canada & Ottawa, 2004.
[7] Rehak M., Pechoucek M., Bartos K., Grill M., Celeda P., and Krmick V., An Intrusion Detection System for High-Speed Networks, in Proceedings of National Institute of Informatics , Berlin, pp. 65-74, 2008.
[8] Almgren M., Lindqvist U., and Jonsson E., A Multi-Sensor Model to Improve Automated Attack Detection, in Proceedings of Lecture Notes in Computer Science , Berlin, pp. 291-310, 2008.
[9] Laleh N. and Azgomi M., A Taxonomy of Frauds and Fraud Detection Techniques, in Proceeding of Communications in Computer and Information Science , Berlin, pp. 256-267, 2009.
[10] Encyclopedia-PC Magazine, available at: http://www.pcmag.com, last visited 2012.
[11] Magalhaes R., Host-Based IDS vs. Network- Based IDS (Part 2-Comparative Analysis), available at: http://www.windowsecurity.com/ articles/hids_vs_nids_part2.html, last visited 2003.
[12] Mudd S., Garcia J., and Fernandez A., Wireless Network Structure-v1.3, available at: http://www.wl0.org/~sjmudd/wireless/network- structure/english/article.html, last visited 2002.
[13] AbdRazak S., Furnell S., Clarke N., and Brooke P., A Two-Tier Intrusion Detection System for Mobile Ad Hoc Networks A Friend Approach, in Proceedings of Lecture Notes in Computer Science , Berlin, pp. 590-595, 2006.
[14] Zhang Z. and Shen H., A Brief Observation- Centric Analysis on Anomaly-Based Intrusion Detection , in Proceedings of Lecture Notes in Computer Science , Berlin, pp. 178-191, 2005. Jatinder Singh Received his M.Tech degree from Punjabi University, Patiala in 2003. He is a prolific author in the field of computer engineering. He has also won best research scholal award by UGC and management excellence award by MIDI, Punjab. He published 50 National and International research papers over the years as wel l as 20 highly acclaimed text and research books. He is also a member of several professional scientific organizations and has lectured widely at academic institutions in India and Abroad. Presently is work ing as a Director in Universal Institute of Engineering & Technology Lalru CHD. Lakhwinder Kaur received the ME degree from TIET, Patiala, Punjab, in 2000 and Ph.D. degree from PTU, Jalandhar in 2007 both in computer science and engineering. She has been in the teaching profession since 1992. Presently, she is working as Reader in the Department of CSE, University College of Engineering, Punjabi University, Patiala (Pb). H er research interests include image compression and denoising, grid computing and wavelets. Savita Gupta received the B.Tech. degree from TITS, Bhiwani (Haryana), in 1992, M.E. degree from TIET, Patiala, Punjab, in 1998 both in computer science and engineering. She obtained her Ph.D. degree from PTU, Jalandhar in 2007 in the field of ultrasound image processing. She ha s been in the teaching profession since 1992. Present ly, she is working as professor in the Department of CS E, University Instiute of of Engineering & Technology, Panjab University, Chandigarh. Her research interes ts include image processing, image compression and denoising, and wavelet applications.