CFP last date
20 January 2025
Reseach Article

Trust Aware Intrusion Detection System based on Cluster

by Devendra Singh, S.S. Bedi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 131 - Number 7
Year of Publication: 2015
Authors: Devendra Singh, S.S. Bedi
10.5120/ijca2015907302

Devendra Singh, S.S. Bedi . Trust Aware Intrusion Detection System based on Cluster. International Journal of Computer Applications. 131, 7 ( December 2015), 7-13. DOI=10.5120/ijca2015907302

@article{ 10.5120/ijca2015907302,
author = { Devendra Singh, S.S. Bedi },
title = { Trust Aware Intrusion Detection System based on Cluster },
journal = { International Journal of Computer Applications },
issue_date = { December 2015 },
volume = { 131 },
number = { 7 },
month = { December },
year = { 2015 },
issn = { 0975-8887 },
pages = { 7-13 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume131/number7/23459-2015907302/ },
doi = { 10.5120/ijca2015907302 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:26:53.052323+05:30
%A Devendra Singh
%A S.S. Bedi
%T Trust Aware Intrusion Detection System based on Cluster
%J International Journal of Computer Applications
%@ 0975-8887
%V 131
%N 7
%P 7-13
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Mobile Ad hoc Networks (MANET) has gained substantial research interest, owing to its easy deployment and inexpensiveness. However, the security of the network is the major concern, because of the absence of the central authority. This work addresses these issues by incorporating the trust mechanism in the cluster formation and routing. The chief node is selected on the basis of four trust parameters such as energy, packet delivery ratio, neighbour count and mobility. The chief node kicks off the misbehaving nodes during the process of routing. The proposed work is proved to be resilient against replay and sybil attacks. The performance of this work is evaluated in terms of several popular performance metrics and the system proves its efficacy.

References
  1. S. Gwalani, K. Srinivasan, G. Vigna, E. M. Beding-Royer, and R. Kemmerer. An intrusion detection tool for AODV-based ad hoc wireless networks. In proc. of the IEEE Computer Security Applications Conference (CSAC), 2004.
  2. T. Anantvalee and J. Wu. A survey on intrusion detection in mobile ad hoc networks. Wireless/Mobile Network Security, 2006.
  3. Y. Hu, A. Perrig, and D. B. Johnson. Ariadne: A secure on-demand routing protocol for ad hoc networks. In proc. of the ACM International Conference on Mobile Computing and Networking (MOBICOM), 2002.
  4. P. Ning and K. Sun. How to misuse AODV: A case study of insider attacks against mobile ad-hoc routing protocols. In proc. of the IEEE Information Assurance Workshop, 2003.
  5. Y. Huang and W. Lee. A cooperative intrusion detection system for ad hoc networks. In proc. of the ACM Workshop on Security of Ad Hoc and Sensor Networks, 2003.
  6. O. Kachirski and R. Guha. Efficient intrusion detection using multiple sensors in wireless ad hoc networks. In proc. of the IEEE Hawaii International Conference on System Sciences (HICSS), 2003.
  7. L. Ramachandran, M. Kapoor, A. Sarkar and A. Aggarwal, “Clustering Algorithms for Wireless Ad Hoc Networks,” In Proceeding: Workshop on Discrete Algorithms and Methods for Mobile Computing and Communications, Boston, 2000, pp. 54-63.
  8. A.Ephremides, J. E. Wieselthier and D.J. Baker. “A Design Concept for Reliable Mobile Radio Networks with Frequency Hopping Signaling,”IEEE, 1987, pp. 56-73.
  9. M. Gerla and J.T. Tsai. “Multicluster, Mobile, Multimedia Radio Network. Wireless Networks,” 1995.
  10. S. Basagni, “Distributed clustering for ad hoc networks,” 1999.
  11. M. Chatterjee, S.K. Das, D. Turgut, “WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks,” Vol.5, No. 2, 2002, pp. 193-204.
  12. W. Choi, M. Woo, “A Distributed Weighted Clustering Algorithm for Mobile Ad Hoc Networks,” Advanced International Conference on Telecommunications, 2006.
  13. C. R. Lin and M. Gerla., “Adaptive Clustering for Mobile Wireless Networks,” IEEE Journal on Selected Areas in Communications, Vol. 15, No. 7, 1997, pp.1265-1275.
  14. A. Ramalingam, S. Subramani and k. Perumalsamy., “Associativity-based Cluster Formation and Cluster Management in Ad Hoc Networks,” HiPC, 2002.
  15. Chen Y.Z.P. and Liestman A.L., "Approximating Minimum Size Weakly-Connected Dominating Sets for Clustering Mobile Ad Hoc Networks, 3rd International Symposium on Mobile Ad Hoc Networks and Computing, pp. 165-172, 2002.
  16. Julisch K., "Clustering Intrusion Detection to Support Root Cause Analysis", ACM Transactions on Information and System Security, Vol.6, No.4, pp:443-471,2003.
  17. Nikulin V., "Weighted Threshold based Clustering for Intrusion Detection Systems", International Journal of Computational Intelligence and Applications, Vol. 6, No. 1, pp. 1-19, 2006.
  18. Luo, M., Li, X. and Xie, S., "An Intrusion Detection Research based on Spectral Clustering", 4th International Conference on Wireless Communications, Networking and Mobile Computing, WiCOM' 08, pp: 1-4, 2008.
  19. Jianliang M., Haikun S., Ling B., "The application on intrusion detection based on K-means cluster algorithm", International Forum on Information Technology and Applications, IFITA '09, Vol.1, pp: 150-152, 2009.
  20. Aiguo Chen, Guoai Xu, Yixian Yang, "A Cluster-Based Trust Model for Mobile Ad Hoc Networks", 4th International Conference on Wireless Communications, Networking and Mobile Computing, pp. 1-4, 2008.
  21. Feng Li, Ju Wu, "Uncertainty Modelling and Reduction in Manets", IEEE Transactions on Mobile Computing, Vol.9, No.7, pp.1035-1048, 2010.
  22. Serique, L.F.S. and De Sousa, R.T., "Evaluating Trust in Ad hoc Network Routing by Induction of Decision Trees", IEEE Latin America Transactions, Vol. 10, No. 1, pp.1332-1343, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

MANET trust routing replay attack sybil attack.