LightRL-AD: A Lightweight Online Reinforcement Learning Approach for Autonomous Defense against Network Attacks
Fengyuan Shi, Zhou Zhou, Guo Jiang, Renjie Li, Zhongyi Zhang, Shu Li, Qingyun Liu, Xiuguo Bao · 2024
With the rapid growth of the Internet, network structure has become increasingly complex, leading to more diverse and impactful network attacks. Traditional methods of detecting and defending against network attacks struggle with increasingly complex situations due to human decision-making processes. Recent research has started exploring autonomous defense mechanisms for network attacks within software-defined network (SDN) environments. However, these methods typically employ complex reinforcement learning techniques, making them challenging to implement in online deployment environments. In this paper, we propose LightRL-AD, a lightweight online reinforcement learning approach for autonomous defense against network attacks in SDN. LightRL-AD integrates a machine learning-based Intrusion Detection System (IDS), a reinforcement learning-based Intrusion Prevention System (IPS), and a Moving Target Defense (MTD) mechanism. The ML-based IDS classifies network flows into categories such as malicious or benign, while the RL-based IPS utilizes the SARSA algorithm to determine and execute appropriate defensive actions, ensuring robust network security. We employ specific hardware and software to establish a simulated SDN network for our experiments. And we implement LightRL-AD in the network and evaluate its performance. Experimental results demonstrate that LightRL-AD performs better to defend against slow-rate DDoS attacks autonomously.