Matyas–Meyer Oseas based device profiling for anomaly detection via deep reinforcement learning (MMODPAD-DRL) in zero trust security network

Abstract

The exposure of zero trust security in the Industrial Internet of Things (IIoT) increased in importance in the era where there is a huge risk of injection of malicious entities and owning the device by an unauthorized user. The gap in the existing approach of zero trust security is that continuous verification of devices is a time-consuming process and adversely affects the promising nature of the zero-trust model. Every time the node enters, even if the node is a member of the network, authorization of the node is necessary to ensure authenticity. This verification section of zero trust hinders the seamless working of the IIoT infrastructure. Therefore, the main objective of this paper is to propose the solution for the above-mentioned problem by enabling “device profiling” via deep reinforcement learning so that the same device can be identified and permitted access without hindering the working of Industrial Internet of Things infrastructure. The overall proposed approach works in different phases including the compression function for ensuring data confidentiality and integrity, then the device profiling is performed based on the features a device possesses, and lastly, deep reinforcement learning for anomaly detection. To test and validate the proposed approach, extensive experimentations were performed using measures such as false positive rate, data confidentiality rate, data integrity rate, and network access time, and results showed that the proposed technique titled “MMODPAD-DRL” outperforms the existing approaches in false positive rate by 27%, data confidentiality rate by 4% and data integrity rate by 3%, in addition, lessen the network access time by 20%.