A Learning Automata Based Mechanism to Mitigate Energy Draining Data Flooding Attack in MANETs
DOI:
https://doi.org/10.26438/ijcse/v6i1.196202Keywords:
MANETs, Flooding, Learning Automata, AODV, DoS, QoSAbstract
The inherent characteristics of mobile ad hoc networks make them susceptible to different types of malicious flooding attacks. Data flooding attack is one of them. Such flooding attacks deplete the network bandwidth and other precious resources to a large extent, creating barriers for future communication among legitimate nodes, paralyzing network operations and leading to Denial of Service (DoS) situation. Learning Automata theory has emerged as a useful tool for performing research activities targeting wireless mobile ad hoc networks. In this paper, we present LA-FIDS - a Learning Automata based Flooding Intrusion Detection System that mitigates the effect of data flooding attack in MANETs by detecting and isolating energy draining malicious node from the communication path. The proposed work is an effort to fill the gap mentioned F-IDS scheme, for not mitigating data flooding attack. The proposed mechanism is implemented in NS 2.35 and simulation results show a considerable improvement in network performance as compared to F-IDS scheme in term of various network performance metrics.
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