Border Collie Cat Optimization for Intrusion Detection System in Healthcare IoT Network Using Deep Recurrent Neural Network

Abstract Attacks are the major problems in the Internet of Things (IoT) applications and communication networks. The undetected intruders affect the availability of the system for end-users, increase identity theft and data breaches. Hence, it is required to detect the attacks in the IoT systems to ensure effective defense and security. In this research, the Border Collie Cat Optimization-based Deep Recurrent Neural Network is proposed to detect intrusion in the IoT networks. Here, the proposed Border Collie Cat Optimization algorithm is derived by the integration of Border Collie Optimization and Cat Swarm Optimization. At first, the messages are authenticated at the authentication phase using the hashing and encryption function. After authenticating the device, the communication between the server and user is carried out at the communication phase to make the IoT device eligible for data transfer within the network. Then, the Deep Recurrent Neural Network classifier is employed to detect the intruders in the IoT network in such a way that the training process is carried out using the proposed Border Collie Optimization algorithm. The proposed approach obtained higher performance with the metrics, like detection rate, sensitivity, specificity and accuracy with the values of 0.9375, 0.9539, 0.8791 and 0.9263, respectively.