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

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 periodically. The great challenges posed to healthcare WBANs are the black hole and sink hole attacks. Data from deployed sensor nodes are attracted by sink hole or black hole nodes while grabbing the shortest path. Identifying this issue is quite a challenging task as a small variation in medicine intake may result in a severe illness. This 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 issues. First, the gathered training data feature count is reduced through data pre-processing in the PCS. Second, the pre-processed features are sent to the MK-Means algorithm for training the data and promoting classification. Third, 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 algorithm. This 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.