Game-Theoretic Actor-Critic-Based Intrusion Response Scheme (GTAC-IRS) for Wireless SDN-Based IoT Networks

In the era of the Internet of Things (IoT), reinforcement learning (RL)-based techniques are promising candidates to handle the intrusion response through the interaction between the IoT device and its environment. Given a large number of devices in the IoT network, wireless software-defined networking (W-SDN) is widely agreed to be introduced to facilitate network management, such as launching a new intrusion response scheme (IRS) on massive IoT devices. To guarantee the scalability and security of IoT services on the extended devices in the W-SDN-based network, a distributed RL-based IRS is proposed in this article, called game-theoretic actor-critic-based IRS (GTAC-IRS). GTAC-IRS employs a game-theoretic-based response selection matrix, aiming at reducing training time and facilitating the convergence of the response scheme. GTAC-IRS constructs a well-designed state representation of observed environment status, a low-dimension response matrix, and a simplified response selection policy to lower the complexity of the algorithms. Simulation results reveal that benefiting from local environment observation, GTAC-IRS achieves effective intrusion response without sophisticated feature engineering. Instead of the "warm-start" training adopted in conventional RL-based IRS, the low-dimension response matrix in GTAC-IRS can significantly improve the convergence speed. Thus, GTAC-IRS outperforms other popular IRSs in terms of the response time under the circumstance of the node's behavior changing or malicious nodes ratio changing.