Learning-to-Adaptation for Security Service in Industrial IoT: An AI-Enabled Slice-Specific Solution

Abstract

Network slicing is the key enabler for the 5G Industrial Internet of Things (IIoT), allowing tailored services and security guarantees for vertical industries. With the advent of 5G-Advanced (5G-A) and 6G era, the number of slices will increase significantly, leading to more diverse security requirements given different slice features. To provide adaptive security management spanning multiple slices in IIoT, this paper proposes a novel slice-specific secure IIoT (SSIOT) architecture with an AI-enabled solution. The SSIOT architecture separates the control and data planes, where the control plane orchestrates the Security Service Function Chains (SSFC) across network slices and the data plane analyzes the slice-specific features like traffic patterns, resource SLA guarantees, and Virtual Security Network Function (VSNF) dependencies. To extract these spatial-temporal features from the dynamic IIoT environments, we facilitate the powerful deep reinforcement learning (DRL) methods and propose a structural GS2L approach. GS2L is maliciously designed with the core principles of graph convolutional network (GCN) and Gated Recurrent Unit (GRU), enabling a thorough understanding of physical resource distribution and the request dynamics across slices. Extensive experiments are conducted in diverse IIoT slices with the real-world USNet and fat-tree topologies. Simulation results demonstrate that GS2L outperforms state-of-the-art learning and heuristic benchmarks, showcasing an overall 15.2% improvement with efficient and stable resource utilization.