Cyber attack detection in healthcare data using cyber-physical system with optimized algorithm

A medical cyber-physical system (MCPS) integrates medical sensor devices with cyber (information) components, which creates a sensitive approach and provides security. The MCPS plays a vital role in hospitals by detecting attacks and protecting patients' medical information. Many research projects have been carried out to detect attacks on the generation of medical information in MCPS. The issues with existing algorithms are their inefficiency and time-consuming maximization of error rates. To overcome the challenges, this paper proposes detecting the attack using the fuzzy C-Means algorithm with artificial bee colony optimization (FCM-ABC). The novelty of the work is flexibility in deciding whether data points belong to actual users or attackers using the fuzzy c means degree [0,1] measuring technique of cluster formation. ABC is used in self-organizing the clusters with ABC collective intelligence. It monitors health information using sensor networks. The MCPS model interconnects the sensor devices remotely and collects the information. SVM has an accuracy rate of 76.32%, FCM has an accuracy rate of 81.34%, LSTM has an accuracy rate of 86.22%, and our proposed work, FCM-ABC, has an accuracy rate of 93.34%.