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An Efficient Clustered M-path Sinkhole Attack Detection (MSAD) Algorithm for Wireless Sensor Networks
P. Shanmugaraja, Manish Bhardwaj, Abolfazl Mehbodniya, Y. Sharmasth Vali and Pundru Chandra Shaker Reddy
Wireless sensor networks (WSNs) are made up of low-energy tiny sensor nodes that can monitor specific environment and transfer data among themselves without the use of a physical media. With the rapid improvements in information technology in recent years, there has been a growing interest among various enterprises in using wireless sensor technologies. Sensor nodes in a WSN with limited energy detect an event, gather data, and send it to the sink node, which is the base node, for further processing and assessment. The design and placement of the sink node can affect a few aspects of WSNs, such as energy consumption and lifetime. Despite their many advantages, WSNs are regarded vulnerable and unprotected. Sinkhole, an adversary attack places serious dangers to the security of such networks within a vast class of varied security assaults that may damage the system’s performance. A sinkhole attack is one of the most harmful types of intrusions because it introduces a compromised or created node into the network that tries to entice network traffic by displaying its incorrect and fraudulent routing update. It can also be utilised to send the base station (BS) bogus or fraudulent information. The paper proposes an efficient algorithm for sinkhole attack detection called M-path Sinkhole Attack Detection (MSAD), a solution to the sinkhole problem in WSN. It is suitable for devices with limited resources like sensors. For energy saving in WSNs, the proposed system applies the concept of clustering, and then the proposed simple and light algorithm for intrusion detection algorithm is applied. The proposed method is evaluated against recent algorithms C-LEACH, S-LEACH, ABC and MS-LEACH algorithms and it is found that the proposed method detects the attack with an accuracy rate of 97%, which is higher than other algorithms.
Keywords: Sinkhole, intrusion detection, clustering, sink node, WSN
DOI: 10.32908/ahswn.v55.8849