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Reseach Article

An Immune Inspired Approach for Detecting Packet Drop Attacks in MANET

by Deepak Kr, T. V. P.sundararajan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 8
Year of Publication: 2012
Authors: Deepak Kr, T. V. P.sundararajan
10.5120/9300-3516

Deepak Kr, T. V. P.sundararajan . An Immune Inspired Approach for Detecting Packet Drop Attacks in MANET. International Journal of Computer Applications. 58, 8 ( November 2012), 6-12. DOI=10.5120/9300-3516

@article{ 10.5120/9300-3516,
author = { Deepak Kr, T. V. P.sundararajan },
title = { An Immune Inspired Approach for Detecting Packet Drop Attacks in MANET },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 8 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number8/9300-3516/ },
doi = { 10.5120/9300-3516 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:01:54.851986+05:30
%A Deepak Kr
%A T. V. P.sundararajan
%T An Immune Inspired Approach for Detecting Packet Drop Attacks in MANET
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 8
%P 6-12
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Security is one of the major issues in the case of wired and wireless networks. The rapid proliferations in the area of Mobile Adhoc Networks (MANETs) have changed the landscape of network security. MANETs are self organizing, infrastructure less, multi hop networks where the constituent nodes act as both hosts and routers simultaneously. Dynamic topological changes and constantly moving nodes in the network make MANETs more vulnerable to a variety of attacks than traditional networks. In this paper we deal with misbehavior nodes in MANETs that drop packets instead of forwarding them towards the destination. To defend against this attack we propose a novel intrusion detection system (IDS) that is motivated by the human immune system. Recently, IDS techniques that are inspired from the Immune System (IS) of human beings are gaining importance since immune systems are robust, accurate and highly adaptable in detecting any new intrusion. From the results it is shown that the proposed immune AODV provides better performance than the normal AODV in the presence of packet dropping nodes in the network.

References
  1. C. Prehofer and C. Bettstetter, "Self-organization in communication networks: Principles and design paradigms," IEEE Communications Magazine, 43:78–85, July 2005.
  2. Chaoqun Lin, Yang Zhou, Yunhua Xiao and Guang Sun, "Encryption Algorithm of RSH", Information Technology Journal, 10(3):686-690, 2011.
  3. Y. Zhang, W. Lee, and Y. Huang, "Intrusion Detection Techniques for Mobile Wireless Networks," ACM/Kluwer Wireless Networks Journal (ACM WINET), 9: 546- 556, 2003.
  4. S. Meenakshi and S. K. Srivatsa, "A Distributed Framework with less False Positive Rate against Distributed Denial of Service Attack", Information Technology Journal, 6:1139-1147, 2007.
  5. A. Vani and D. Sreenivasa Rao, "Providing of Secure Routing against Attacks in MANETs", Int. J. Computer Applications, 24: 16-25, June 2011.
  6. S. Buchegger and J. -Y. Le Boudec, "Performance analysis of the CONFIDANT protocol: Cooperation of nodes—Fairness in distributed ad hoc networks," in Proc. IEEE/ACM Symposium on Mobile Ad hoc Networking and Computing (MobiHOC), Lausanne, Switzerland, pp. 80–91, 2002.
  7. Suchita Gupta and Ashish Chourey, "Performance Evaluation of AODV Protocol under Packet Drop Attacks in MANET", in Int. J. Research in Computer Science, 2: 21-27, 2011.
  8. Srinivasa Rao D. , Pandurang Vital T. , Sriram T. V. S. , "Detection of Routing Anomaly using IDS Architecture based on Agents and Clusters in MANETs", Int. J. Computer Applications, 26: 36-40, July 2011.
  9. Z. Muda, W. Yassin, M. N. Sulaiman and N. I. Udzir, "A K-Means and Naïve Bayes Learning approach for better intrusion detection", Information Technology Journal, 10(3):648-655, 2011.
  10. AISWeb - The Online Home of Artificial Immune Systems (http://www. artificial-immune-systems. org/).
  11. S. A. Hofmeyr and S. Forrest, "Architecture for an artificial immune system," in proceeding of Evolutionary Computation. , 7: 45–68, 2000.
  12. S. A. Hofmeyr, "An immunological model of distributed detection and its application to computer security," Ph. D. dissertation, Dept. Computer. Science, Univ. New Mexico, Apr. 1999.
  13. Hu Zhengbing, Zhou Ji, Ma Ping, "A Novel Anomaly Detection Algorithm Based on Real-Valued Negative Selection System", in proceedings of the IEEE workshop on Knowledge Acquisition and Modeling, pp. 499-502, 2008.
  14. Chen Jinyin, Yang Dongyong, "A Study of Detector Generation Algorithms Based on Artificial Immune in Intrusion Detection System", in WSEAS Transactions on Biology and Biomedicine 4: 29-35, 2011.
  15. Lu Hong, "Artificial immune system for anomaly detection", in proceedings of the IEEE workshop on Knowledge Acquisition and Modeling, pp. 340-343, 2008.
  16. S. Sarafijanovic, and J-Y. Le Boudec, "An Artificial Immune System Approach with Secondary Response for Misbehavior Detection in Mobile ad hoc Networks", in Proc. of IEEE Transactions On Neural Networks, September , 16: 1076-1087, 2005.
  17. Xianjin Fang, L. L. , "An Improved Artificial Immune Approach To Network Intrusion Detection", in proceedings of international conference on Advanced Computer Control, 2: 39-44, 2010.
  18. C. Perkins, E. Belding-Rover an S. Das, "RFC-3561: Ad Hoc On-Demand Distance Vector (AODV) Routing". Available at: www. ietf. org/ref/ref3561. txt, July2003.
  19. Network Simulator-2, Available at: www. isi. edu/nsnam/ns.
Index Terms

Computer Science
Information Sciences

Keywords

Security Mobile Ad Hoc Networks (MANET) Intrusion detection Systems (IDS) Artificial Immune Systems (AIS) Immune system or Natural Immune System