Improving intrusion detection in cloud-based healthcare using neural network

Abstract

This research introduces an efficient smart security solution with the effective healthcare technique for Arrhythmia classification using the cloud computing scenario. Initially, the ECG of the patient in both the static and dynamic environment is collected using sensors and is stored in cloud, in which the clustering of the network and cluster head (CH) selection is devised using the Hybrid tempest optimization algorithm. In order to ensure the security for the collected healthcare data, the intrusion detection model is devised and implemented in the cloud storage for secure data access using the Neural network (NN), where the NN is trained using the Hybrid tempest optimization algorithm. The access is granted to the legitimate users for acquiring the data for diagnosis. The hybrid tempest-NN method is analyzed through accuracy, sensitivity, and specificity and obtained the maximal values of 95.72%, 96.78%, and 95.29% respectively.