Open-Set Intrusion Detection Solution for Industrial Internet of Things Based on Deep Spiking Q-Networks

Abstract

: The rapid growth of the Industrial Internet of Things (IIoT) has become a cornerstone of high-quality global economic development. By integrating sensor networks, edge computing, and cloud intelligence, IIoT has emerged as a key enabler for smart manufacturing and digital transformation across industries. However, this technological advancement introduces significant cybersecurity challenges that render traditional intrusion detection systems inadequate for IIoT environments. To address this critical gap, we propose a deep spiking Q-network (DSQN)-based intrusion detection system (DSQN-IDS) for the IIoT, formulating unknown intrusion detection as a Markov decision process (MDP). The system employs a hierarchical multi-stage decision-making framework integrating conditional variational autoencoders (CVAE) for feature extraction, deep Q-networks (DQN) for reinforcement learning-based decision-making, and spiking neural networks (SNNs) for energy-efficient classification. We train the DSQN using a multi-layer perceptron (MLP) to approximate the state-action value function, and leverage the event-driven nature of SNNs—where neurons only spike when their membrane potential exceeds a threshold—to minimize energy consumption. Extensive experiments on IIoT datasets demonstrate that our approach achieves superior performance in balancing detection accuracy, energy efficiency, and model stability when identifying unknown attacks compared to state-of-the-art methods.