Secure Healthcare Model Using Multi-Step Deep Q Learning Network in Internet of Things

Abstract

Internet of Things (IoT) is an emerging networking technology that connects both living and non-living objects globally. In an era where IoT is increasingly integrated into various industries, including healthcare, it plays a pivotal role in simplifying the process of monitoring and identifying diseases for patients and healthcare professionals. In IoT-based systems, safeguarding healthcare data is of the utmost importance, to prevent unauthorized access and intermediary assaults. The motivation for this research lies in addressing the growing security concerns within healthcare IoT. In this proposed paper, we combine the Multi-Step Deep Q Learning Network (MSDQN) with the Deep Learning Network (DLN) to enhance the privacy and security of healthcare data. The DLN is employed in the authentication process to identify authenticated IoT devices and prevent intermediate attacks between them. The MSDQN, on the other hand, is harnessed to detect and counteract malware attacks and Distributed Denial of Service (DDoS) attacks during data transmission between various locations. Our proposed method’s performance is assessed based on such parameters as energy consumption, throughput, lifetime, accuracy, and Mean Square Error (MSE). Further, we have compared the effectiveness of our approach with an existing method, specifically, Learning-based Deep Q Network (LDQN).

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“…In the realm of robotics, an application of a Deep Q-Learning algorithm to enhance the positioning accuracy of an industrial robot was studied in [ 50 ]. Additionally, DQL’s strategic decision-making prowess was applied in healthcare to optimize the security and privacy of healthcare data in IoT systems, focusing on authentication, malware, and DDoS attack mitigation, and evaluating performance through metrics like energy consumption and accuracy [ 51 ]. In the financial sector, the study [ 52 ] introduced an automated trading system that combines reinforcement learning with a deep neural network to predict share quantities and employs transfer learning to overcome data limitations.…”