SWARM INTELLIGENCE TECHNIQUE FOR SINKHOLE ATTACK DETECTION IN WIRELESS SENSOR NETWORK USING ABC ALGORTHM

Computer Network Engineering

Data sensing, processing and communicating is an inevitable requirement in monitoring environment or physical conditions. The rudiment of WSN satisfies this stipulation of monitoring systems in different areas and is widely favored in numerous sensitive systems. Security concerns is a fervently discussed topic by the research community as the probability of vulnerable attacks is more in WSN. This research work focuses on enhancing the security of WSN by efficiently detecting sinkhole attack. Sinkhole attack is more intense as it results in bringing in other varieties of intrusions. Detection schemes depend on computational intelligence and Swarm Optimization is a commonly preferred approach. In our work, existing intrusion detection algorithm using Enhanced Particle Swarm Optimization (EPSO) was compared with a proposed Artificial Bee Colony (ABC) technique. The proposed ABC approach yielded improvement in terms of Detection rate, False Alarm rate, Packet delivery ration, Message drop and Average delay. These results promise a better intrusion detection scheme than the existing EPSO technique.

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