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Black Hole and Sink Hole Attack Detection in Wireless Body Area Networks

Computers, Materials & Continua 2021年第8期68卷 1949-1965页

作者： Rajesh Kumar Dhanaraj Lalitha Krishnasamy Oana Geman Diana Roxana Izdrui School of Computing Science and Engineering Galgotias University201310India Department of Information Technology Kongu Engineering CollegeErode638060India Department of Health and Human Development Stefan cel Mare UniversityRomania Department of Computers Electronics and AutomationStefan cel Mare UniversityRomania

In Wireless Body Area Networks(WBANs)with respect to health care,sensors are positioned inside the body of an individual to transfer sensed data to a central station *** great challenges posed to healthcare WBANs are the black hole and sink hole *** from deployed sensor nodes are attracted by sink hole or black hole nodes while grabbing the shortest *** this issue is quite a challenging task as a small variation in medicine intake may result in a severe *** work proposes a hybrid detection framework for attacks by applying a Proportional Coinciding Score(PCS)and an MK-Means algorithm,which is a well-known machine learning technique used to raise attack detection accuracy and decrease computational difficulties while giving treatments for heartache and respiratory ***,the gathered training data feature count is reduced through data pre-processing in the ***,the pre-processed features are sent to the MK-Means algorithm for training the data and promoting ***,certain attack detection measures given by the intrusion detection system,such as the number of data packages trans-received,are identified by the MK-Means *** study demonstrates that the MK-Means framework yields a high detection accuracy with a low packet loss rate,low communication overhead,and reduced end-to-end delay in the network and improves the accuracy of biomedical data.

关键词： Wireless body area network black hole attack sink hole attack proportional coinciding score intrusion detection correlation rate

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