Critical concerns regarding the security and privacy of information transmitted within Internet of Medical Things systems have increased greatly, since these systems manage and generate substantial amounts of sensitive private data. Current traditional security methods have not yet adapted to evolving cyber threats, making the need for data security in medical settings crucial. Recently, a security framework based on blockchain technology and distributed reinforcement learning has been developed to address these challenges. The new framework ensures that data are stored securely and transmitted reliably while minimizing resource usage, and it also enables security measures to adapt to changing threat patterns and enhance system resilience against attacks. In summary, the new method demonstrated improved memory consumption and transaction latency compared to existing approaches, while maintaining high data throughput. This work was published in Intelligent Computing , a Science Partner Journal, under the title " Privacy-Preserving Strategies in the Internet of Medical Things Using Reinforcement Learning and Blockchain " by Dounia Doha and Ping Guo.
The new method achieved an accuracy of 88% in detecting address resolution protocol man-in-the-middle attacks, which is higher than traditional methods such as support vector machines (83%), random forests (75%), and decision trees (68%). The latency was also the lowest, at 45 ms, whereas the older models suffered from values ranging between 85 and 110 ms. The false-positive rate was the lowest for the new framework at 6%, compared to 12–20% for the others. Resource utilization efficiency reached 80%, though memory usage was also the highest at 320 MB. In the Mirai botnet dataset, the new method demonstrated clear advantages by continuously refining detection strategies from incoming data, allowing it to respond to emerging threats more effectively than static models.
The authors based their reinforcement learning on a deep Q network and used Hyperledger Fabric as the foundational blockchain. The framework outperformed traditional machine learning approaches in adaptability and attack detection accuracy within Internet of Medical Things environments. The deep Q network was selected as the reinforcement learning algorithm because it balances adaptability and computational cost better than policy-based methods such as proximal policy optimization and asynchronous advantage actor–critic. While policy-based methods require continuous updates to both actor and critic networks, deep Q networks use a Q-value function, which reduces computational overhead and avoids the need for complex continuous-action modeling required in algorithms like deep deterministic policy gradient. This makes it better suited for resource-constrained devices. Hyperledger Fabric, known for its relatively light resource consumption and high transaction throughput, was used to facilitate secure data validation and storage of data from Internet of Medical Things sensors.
Even though this new framework outperformed older methods in terms of adaptability, attack detection accuracy, and suitability for continuous monitoring in high-stakes healthcare applications, further research is needed to increase its applicability and efficiency. At present, the computational and memory demands remain high. Optimizing the computational footprint of reinforcement learning would make the framework more suitable for edge devices and distributed Internet of Things environments with limited resources. Future models may integrate robust security with stronger privacy-preserving techniques, such as federated learning, to safeguard sensitive medical data. Other options, such as hybrid approaches that combine the adaptability of reinforcement learning with the lower resource demands of traditional algorithms, may also provide balanced solutions for Internet of Medical Things environments.
