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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(5): 564-568 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.190

Trust Count Based Authentication Technique for Mitigation of Internal Attacks in MANET

R. Murugan and A. Shanmugam
Abstract—The wireless nature and inherent features of mobile ad hoc networks make them vulnerable to a wide variety of attacks. In an internal attack, the attacker gains the normal access to the network and takes part in the network activities, either by some malicious impersonation to get the access to the network as a new node, or by directly compromising a current node and using it as a basis to conduct its malicious behaviors. In this paper, we develop a cluster based authentication technique to mitigate the internal attacks. The entire network is divided into hierarchical group of clusters, each cluster having a fully trusted cluster head. Each node holds a certificate issued by an offline certificate authority (CA). The Trust Count (TC) for each of the nodes can be estimated periodically for every trust evaluation interval (TEI), based on their access policy (AP). The certificate of a node is renewed or rejected by the cluster head, based on its trust counter value. By simulation results, we show that our proposed technique provides better packet delivery ratio and resilience against node capture.

Index Terms—Access policy, authentication, clustering, node capture attacks, trust count.

R. Murugan is with Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam, India (email: muruganraam75@yahoo.com). A. Shanmugam is with Department of Electronics and Communication Engineering of Bannari Amman Institute of Technology, Sathyamangalam, India.

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Cite:R. Murugan and A. Shanmugam, "Trust Count Based Authentication Technique for Mitigation of Internal Attacks in MANET," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 564-568, 2012.

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