Feature Selection for Detection of Ad Hoc Flooding Attacks
In recent years ad hoc networks have become very attractive for many applications such as tactical and disaster recovery operations. However they are vulnerable to many attacks. The vulnerabilities of wired networks such as denial of service (DoS), eavesdropping, spoofing and the like, becomes more acute in these networks. Especially it is hard to differentiate DoS attacks in these highly dynamic systems. In this research, we design an intrusion detection model using Support Vector Machines (SVM) in order to detect a popular DoS attacks on these networks, namely ad hoc flooding attacks. We evaluate its performance on simulated networks with varying traffic and mobility patterns. Furthermore we investigate to choose the relevant features using Genetic Algorithms (GA) in order to increase SVM performance on detection of these attacks.
KeywordsGenetic Algorithm Support Vector Machine Feature Selection False Positive Rate Intrusion Detection
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- 1.Tseng, C.-Y., Balasubramayan, P., Ko, C., Limprasittiprn, R., Rowe, J., Lewitt, K.: A Specification-Based Intrusion Detection System for AODV. In: Proceedings of the ACM Workshop on Security in Ad Hoc and Sensor Networks, pp. 125–134 (2003)Google Scholar
- 3.Vigna, G., Srinivasan, K., Belding-Royer, E.M., Kemmerer, R.A.: An Intrusion Detection Tool for AODV-Based Ad hoc Wireless Networks. In: Proceedings of the 20th Annaual Computer Security Applications Conference, pp. 16–27. IEEE Computer Society (2004)Google Scholar
- 4.Perkins, C.E., Royer, E.M.: Ad-hoc on demand distance vector routing. In: Proceedings of IEEE Workshop on Mobile Computer Systems, pp. 90–100 (1999)Google Scholar
- 7.Sun, B., Wu, K., Pooch, U.: Zone-based intrusion detection for mobile ad hoc networks. Int. J. of Ad Hoc and Sens. Wirel. Netw. 2 (2003)Google Scholar
- 8.Huang, Y., Fan, W., Lee, W., Yu, P.S.: Cross-feature Analysis for Detection Ad-hoc Routing Anomalies. In: Proceedings of the 23rd International Conference on Distributed Computing Systems, pp. 478–487. IEEE (2003)Google Scholar
- 10.Mitrokotsa, A., Tsagkaris, M., Douligeris, C.: Intrusion Detection in Mobile Ad Hoc Networks Using Classification Algorithms. In: Proceedings of the Seventh Annual Mediterranean Ad Hoc Networking Workshop-Advances in Ad Hoc Networking, pp. 133–144. Springer (2008)Google Scholar
- 11.LibSVM: A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm/
- 12.The network simulator, http://www.isi.edu/nsnam/ns/ (cited February 15, 2012)
- 13.BonnMotion: A mobility scenario generatin and analysis tool, http://net.cs.uni-bonn.de/wg/cs/applications/bonnmotion/ (cited February 15, 2012)
- 14.Wroblewski, J.: Finding Minimal Reducts Using Genetic Algorithms. In: Proceedings of the Second Annual Joint Conference on Information Sciences, pp. 186–189 (1995)Google Scholar
- 15.Lanzi, P.L.: Fast Feature Selection with Genetic Algorithms: A Filter Approach. In: Proceedings of IEEE Conference on Evolutionary Computation, pp. 537–540 (1997)Google Scholar
- 18.ecj20: A Java-based Evolutionary Computation Research System, http://cs.gmu.edu/~eclab/projects/ecj/ (cited February 15, 2012)