Adaptive Security Service Provisioning for Industrial IoT: Harnessing Deep Reinforcement Learning Within a Slice-Specific Framework

The Industrial Internet of Things (IIoT) continues to evolve alongside advancements in 5G and beyond 5G communication technologies, and network slicing has emerged as a promising technique to offer isolated slices and tailored services across industrial use cases, which profoundly affects the traditional security solution. As the future IIoT evolves towards the post-5G era, the anticipated growth in network slices will unavoidably exacerbate challenges in security service management, but developing an adaptive and effective security strategy remains a significant challenge. This study introduces a novel slice-specific IIoT (SSIOT) architecture, meticulously crafted to address the unique security needs of each slice based on network function virtualization. To adapt to the SSIOT, an AI-driven GS2L model is presented, which combines graph convolutional network (GCN) with sequence-to-sequence (Seq2Seq) deep reinforcement learning (DRL). The GS2L offers the network topology explanation module, service request analysis module, and slice-specific distribution extraction module, and adeptly navigates the multifaceted Security Service Function Chain (SSFC) embedding conundrum and resource allocation in slice-specific systems. Comprehensive experimental evaluations underscore GS2L's superiority, showcasing its proficiency in delivering augmented QoS satisfaction while ensuring prudent resource utilization over the learning-based and heuristic benchmark.