Two-Phase Deep Q Learning Network for Secure Healthcare Model in the Internet of Things

The Internet of Things (IoT) embodies a revolutionary networking technology that links both living beings and inanimate objects. Its utilization is rapidly growing across diverse sectors, particularly in healthcare, where it is instrumental in enabling healthcare providers and individuals to monitor and diagnose diseases. It is crucial to ensure the security of healthcare data on IoT devices to prevent intermediary attacks and unauthorized access. This study explores the increasing security challenges associated with IoT in the healthcare field. To enhance the security and privacy of healthcare data, we propose a combination of the Two-Phase Deep Q Learning Network (TPDQN) and the Deep Learning Network (DLN). The DLN is primarily focused on authenticating certified IoT devices to prevent intermediate attacks, while the TPDQN is designed to detect and mitigate Distributed Denial of Service (DDoS) attacks and malware as they traverse multiple locations. The effectiveness of the proposed solution is measured using metrics such as mean square error (MSE), longevity, accuracy, throughput, and energy consumption, demonstrating improved performance relative to existing solutions.