A Robust Framework with an Aid of Analytical Modeling to Assess the Distinct Behavioural Pattern of Wormhole Adversary Node in MANET

Abstract

Owing to potential benefits towards disrupting the distinct pattern of MANET communication, wormhole attack is very popular among adversaries. However, unlike other forms of wireless networks, very less research effort has been laid towards strengthening the security systems to mitigate these type of attacks in MANET. Addressing this issue, the study comes up with a novel solution which introduces robust analytical modeling which assists in behavior mapping for wormhole attacker with respect to its distinct fractures and uncertain pattern of communication. The notion of the formulated concept applies statistical learning method to generalize the core strategic behavior of the malicious node and also implies that how the discrete behavior differs from the normal behavior of nodes. The analytical solution also improvises a cost-effective decision based on cognitive learning and malicious node behavioral mapping. The model validation is performed with respect to two different performance parameters such as throughput and overhead-latency. The experimental outcome shows the formulated approach accomplishes a better outcome as compared to the existing baseline.