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

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. As the Internet of Things (IoT) experiences rapid growth and the demand for efficient processing of IoT data at the network edge intensifies, Mobile Edge Computing (MEC) has emerged as a highly promising solution to address these challenges. Virtualized Network Functions (VNFs) in MEC enable the deployment of various services closer to the IoT devices, improving latency and reducing bandwidth consumption. However, the security of VNF‐based IoT MEC systems is a major concern due to the large attack surface and the dynamic nature of the network. This paper proposes a secure and autonomous system for revoking unauthorized access in VNF‐based IoT MEC systems by combining a Reinforcement Learning (RL) based approach with blockchain technology. The proposed model uses a RL agent to learn how to revoke unauthorized access to VNFs in a timely and efficient manner. The model is implemented on a bl