An Architecture for Wireless Intrusion Detection Systems Using Artificial Neural Networks
The majority existing wireless intrusion detection systems identifies intrusive behaviors are based on the exploration of known vulnerabilities called signatures of attacks. With this mechanism, only known vulnerabilities are detected which leads to bringing the necessity of new techniques to add in the system. This work considers an architecture for intrusion detection in wireless network based on anomaly. The system is capable to adapt itself to a profile of a new community of users, as well as recognizing attackswith different characteristics than those already known by the system, by considering changes from normal behavior. The system uses artificial neural networks in the processes of detecting intrusions and taking countermeasures. A prototype is implemented and submitted to some simulations and tests, with three different types of attacks of Denial of Service (DoS).
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- AirDefense. Enterprise class wireless intrusion prevention systems: Requirements and figure of merit. Available at http://www.airdefense.net/whitepapers/. Access on November 2007.
- AirMagnet. Manual do usuário - airmagnet laptop wireless lan analyzer user guide. Available at http://www.airmagnet.com/. Access Jul 2008.
- J. Bellardo and S. Savage. 802.11 Denial-of-Service Attacks: Real Vulnerabilities and Practical Solutions. Proceedings of the USENIX Security Symposium, August 2003.Google Scholar
- P. Charles. Jpcap. Available at http://sourceforge.net/projects/jpcap/.
- D. Dasgupta, J. Gómez, F. González, M. Kaniganti, K. Yallapu, and R. Yarramsetti. MMDS: Multilevel Monitoring and Detection System. Proceedings of the 15th Annual Computer Security Incident Handling Conference.Google Scholar
- J. L. DeBoer and T. Bruinsma. Airsnare. Available at http://home.comcast.net/jay.deboer/airsnare/. Access Dec 2007.
- B. Fenner, G. Harris, and M. Richardson. Libpcap. Available at http://sourceforge.net/projects/libpcap/. Access Dec 2007.
- Finisar. Surveyor wireless. Access Jul 2007. Available at http://investor.finisar.com/ReleaseDetail.cfm?ReleaseID=89597.
- S. Haykin. Redes Neurais: Princípios e Prática. Bookman, Porto Alegre, 2 edition, 2001.Google Scholar
- T. Karygiannis and L. Owens. Wireless network security. Technical Report NIST 800–48, National Institute of Standards and Technology, USA, November 2002. Available at http://csrc.nist.gov/publications/nistpubs/800–94/SP800–48.pdf.
- Kismet. Kismet wireless. Available at http://www.kismetwireless.net.
- R. A. Lackey, J. and J. Goddard. Wireless intrusion detection. Technical report, IBM Global Services, 2003.Google Scholar
- Y. Lim, T. Schmoyer, J. Levine, and H. L. Owen. Wireless Intrusion Detection and Response. Proceedings of the 2003 IEEE Workshop on Information Assurance, March 2003.Google Scholar
- A. Lockhart. Snort-wireless project. Available at http://www.snortwireless.org/. Access Nov 2007.
- MATLAB. The mathworks - MATLAB and simulink for technical computing. Available at http://www.mathworks.com/products/matlab/.
- G. G. Meade. Guidelines for the development and evaluation of IEEE 802.11 intrusion detection systems (IDS)), Technical report, NSA Num I332–005R-2005. 2005Google Scholar
- M. Moradi and M. Zulkernine. A Neural Network Based System for Intrusion Detection and Classification of Attacks. Proceedings of 2004 IEEE International Conference on Advances in Intelligent Systems Theory and Applications, page 6, November 2004.Google Scholar
- D. Pleskonjic. Wireless Intrusion Detection Systems (WIDS). 19th Annual Computer Security Applications Conference, December 2003.Google Scholar
- Red-M. Taking control of wireless. Available at http://www.red-m.com/.
- T. R. Schmoyer, Y. X. Lim, and H. L. Owen. Wireless Intrusion Detection and Response: A case study using the classic man-in-the-middle attack. IEEE Wireless Communications and Networking Conference, March 2004.Google Scholar
- H. Yang, L. Xie, and J. Sun. Intrusion Detection Solution to WLANs. IEEE 6th CAS Symp. On Emerging Technologies: Mobile and Wireless Comm., June 2004.Google Scholar
- L. Yanheng, D. Tian, and B. Li. A wireless intrusion detection method based on dynamic growing neural network. 1st International Multi- Symposium on Computer and Computational Sciences, 2006.Google Scholar