Advertisement

Feature Selection for Detection of Ad Hoc Flooding Attacks

  • Sevil Sen
  • Zeynep Dogmus
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 176)

Abstract

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.

Keywords

Genetic Algorithm Support Vector Machine Feature Selection False Positive Rate Intrusion Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 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
  2. 2.
    Tseng, C.H., Wang, S.-H., Ko, C., Levitt, K.N.: DEMEM: Distributed Evidence-Driven Message Exchange Intrusion Detection Model for MANET. In: Zamboni, D., Kruegel, C. (eds.) RAID 2006. LNCS, vol. 4219, pp. 249–271. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  3. 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. 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
  5. 5.
    Huang, Y.-A., Lee, W.: Attack Analysis and Detection for Ad Hoc Routing Protocols. In: Jonsson, E., Valdes, A., Almgren, M. (eds.) RAID 2004. LNCS, vol. 3224, pp. 125–145. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Zhang, Y., Lee, W., Huang, Y.: Intrusion detection techniques for mobile wireless networks. Wirel. Netw. J. 9(5), 545–556 (2003), doi:10.1023/A:1024600519144CrossRefGoogle Scholar
  7. 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. 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
  9. 9.
    Sen, S., Clark, J.A.: Evolutionary Computation Techniques for Intrusion Detection in Mobile Ad Hoc Networks. Comput. Netw. 55(15), 3441–3457 (2011)CrossRefGoogle Scholar
  10. 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. 11.
    LibSVM: A Library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm/
  12. 12.
    The network simulator, http://www.isi.edu/nsnam/ns/ (cited February 15, 2012)
  13. 13.
    BonnMotion: A mobility scenario generatin and analysis tool, http://net.cs.uni-bonn.de/wg/cs/applications/bonnmotion/ (cited February 15, 2012)
  14. 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. 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
  16. 16.
    Yang, J., Honavar, V.: Feature Subset Selection Using A Genetic Algorithm. IEEE Intell. Syst. 12(2), 44–49 (1998)CrossRefGoogle Scholar
  17. 17.
    Huang, C.-L., Wang, C.-J.: A GA-Based Feature Selection and Parameters Optimization for Support Vector Machines. Expert Syst. Appl. 31, 231–240 (2006)CrossRefGoogle Scholar
  18. 18.
    ecj20: A Java-based Evolutionary Computation Research System, http://cs.gmu.edu/~eclab/projects/ecj/ (cited February 15, 2012)

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer EngineeringHacettepe UniversityAnkaraTurkey
  2. 2.Faculty of Engineering and Natural SciencesSabanci UniversityIstanbulTurkey

Personalised recommendations