Cogni-Sec: A secure cognitive enabled distributed reinforcement learning model for medical cyber-physical system

The advent of the Internet of Things (IoT) has resulted in significant technical development in the healthcare sector, enabling the establishment of Medical Cyber-Physical Systems (MCPS). The increased number of MCPS generates a massive amount of privacy-sensitive data, hence it is important to enhance the security of devices and data transmission in MCPS. Earlier several research studies were undertaken in order to enhance security in healthcare, but none of them could adapt to changing behaviors of data attacks. Here the role of blockchain and Reinforcement Learning (RL) comes into play since it can adjust itself to the nature of changing attacks, thus preventing any kind of attacks. This work proposes a solution, named Cogni-Sec, which employs a decentralized cognitive blockchain and Reinforcement Learning architecture and addresses the security issue. Blockchain is incorporated in the approach for data storage and transmission to increase the degree of security in the MCPS modules. Hyperledger Fabric is applied as the blockchain base which shows transaction query results with nearly 10% increased throughput, 69% less memory consumption, and 15% lower CPU usage when compared to Ethereum. Further security risk at the block mining level within a blockchain network is reduced by introducing distributed Reinforcement Learning architecture in replacement for the miner nodes, which imitates the cognitive behavior of miners in a distributed environment. Different multi-agent learning systems have been evaluated for building the mining agent. Among these, the a3c agent in distributed learning setup yields the optimum cumulative reward with a median value of 54.5 and minimizes the maximum number of data threats.