Abstract
MAC layer misbehavior drastically degrades the network efficiency even in the presence of secure ad hoc routing protocols. Even small number of malicious nodes may cause network partitioning or lead to failure of whole network. Simple attacks such as jamming or disruption on the 802.11 MAC, protocol if not taken care properly, propagated to the network layer. Detecting misbehaving node and punishing them is the only way for network survival. This paper introduces a Misbehavior Detection System (MDS) for MANET based on Artificial Immune System (AIS). Negative Selection technique is used for generating the detectors for identifying deviation from normal behavior. The proposed system detects malicious and selfish nodes performing misbehavior at MAC layer with the ability of learning and detecting new misbehavior. The system performance is evaluated using network simulator NS2 for MANET MAC layer 802.11 protocols over two on demand routing protocols AODV and DSR. Detection rate, false positive rate and Packet delivery rate are used as metrics for evaluation.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Issariyakul, T., Hossain, E.: Introduction to Network Simulator NS2. Springer, Berlin (2012)
Anantvalee, T., Wu, J.: A survey on intrusion detection in mobile ad hoc networks. In: Wireless Network Security, pp. 159–180 (2007)
Forrest, S., Perelson, A.S., Allen, L., Cherukuri, R.: Self-nonself discrimination in a computer. In: Proceedings of the IEEE Symposium on Research in Security and Privacy. IEEE Computer Society Press, pp. 202–212 (1994)
Hofmeyr, S.A.: An Immunological Model of Distributed Detection and it’s Application to Computer Security. Ph.D. thesis, Department of Computer Sciences, University of New Mexico, April 1999
Hofmeyr, S.A., Forrest, S.: Architecture for an artificial immune system. Evol. Comput. 7(1), 45–68 (2000)
Kim, J., Bentley, P.: An evaluation of negative selection in an artificial immune system for network intrusion detection. In: Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, pp. 1330–1337 (2001)
Le Boudec, J.-Y., Sarajanovic, S.: An Artificial Immune System Approach to Misbehavior Detection in Mobile Ad-Hoc Networks. Technical Report IC (2003)
Balachandran, S., Dasgupta, D., Wang, L.: A hybrid approach for misbehavior detection in wireless ad-hoc networks. Published in Symposium on Information Assurance (2006)
Sundararajan, T.V.P., Ramesh, S.M., Maheswar, R., Deepak, K.R.: Biologically inspired artificial intrusion detection system for detecting wormhole attack in MANET. Wirel. Netw. J. 20, 563–578 (2013)
Ansari, M.S.A., Inamullah, M.: Misbehavior detection in mobile ad hoc networks using artificial immune system approach, pp. 1–6 (2011)
de Castro, L.N., Timmis, J.: Artificial immune systems: a novel paradigm to pattern recognition. In: Artificial Neural Networks in Pattern Recognition SOCO 2002, pp. 67–84 (2002)
Gonzalez, F.A., Dasgupta, D.: Anomaly detection using real-valued negative selection. J. Genet. Prog. Evolvable Mach. 4, 383–403 (2003)
Dasgupta, D., Gonzalez, F.: An immunity based technique to characterize intrusions in computer networks. IEEE Trans. Evolut. Comput. 6, 281–291 (2002)
Hofmeyr, S.A., Forrest, S.: Immunity by Design: An Artificial Immune System. GECCO 1999
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Tiwari, S., Mishra, K.K., Saxena, N., Singh, N., Misra, A.K. (2016). Artificial Immune System Based MAC Layer Misbehavior Detection in MANET. In: Sulaiman, H., Othman, M., Othman, M., Rahim, Y., Pee, N. (eds) Advanced Computer and Communication Engineering Technology. Lecture Notes in Electrical Engineering, vol 362. Springer, Cham. https://doi.org/10.1007/978-3-319-24584-3_60
Download citation
DOI: https://doi.org/10.1007/978-3-319-24584-3_60
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24582-9
Online ISBN: 978-3-319-24584-3
eBook Packages: EngineeringEngineering (R0)