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Detection Algorithm for Sinkhole Attack in Body Area Sensor Networks Using Local Information

Wireless Body Area Sensors Networks (BAN) have emerged as new applied wireless networking technology with the development of wearable and implanted sensors. BAN has novel application in healthcare, sports, human activity monitoring, disability assistance and entertainment. BAN is now using for real time monitoring and assistance of the patients. BAN operations are vulnerable to various security attacks, including basic and advance attacks. In this paper, we introduce and illustrate the sinkhole attack in a BAN. Then we propose our sinkhole detection algorithm that utilizes the information from data aggregation algorithm to detect a sink hole attacker. Finally, we analyze the performance of the BAN in terms of throughput, latency and packet breakdown and the performance of our detection algorithm. Simulation results show that this attack could severely degrade (up to 40%) the overall performance of the network. The propose detection algorithm has good performance in terms of high success (85% on average) and low (6% on average) false alarm rates.