Reinforcement learning based blockchain model for revoking unauthorized access in Virtualized Network Functions-based Internet of Things Mobile Edge Computing

Abstract

VNFs boost data processing efficiency in Mobile Edge Computing (MEC)-driven Internet of Things (IoT) for healthcare, smart cities, and industrial automation. VNF-based IoT MEC systems encounter a significant security threat due to unauthorized access, posing risks to data privacy and system integrity. Existing approaches struggle to adapt to dynamic environments and lack tamper-proof enforcement mechanisms. In this work, we propose a novel system combining Reinforcement Learning (RL) and blockchain technology to revoke unauthorized access in VNF-based IoT MEC. We introduce the Integrated Action-selection DRL Algorithm for Unauthorized Access Revocation (IASDRL-UAR), a novel RL approach that excels in dynamic environments by handling both continuous and discrete actions, enabling real-time optimization of security risk, execution time, and energy consumption. A behavior control contract (BCC) is proposed and integrated into the RL system, automating behavior checks and enforcement, streamlining security management, and reducing manual intervention. RL feedback plays a pivotal role in steering dynamic security adjustments, gaining valuable perspectives from user behavior via trust scores in the behavior contract. The security features of the proposed method are analyzed. Performance comparisons reveal a substantial improvement, with the proposed system outperforming existing methods by 30% in terms of throughput, 21.7% in system stability, and 26% in access revocation latency. Additionally, the system demonstrates a higher security index, energy efficiency, and scalability.