Packet-Level and Flow-Level Network Intrusion Detection Based on Reinforcement Learning and Adversarial Training

Abstract

Powered by advances in information and internet technologies, network-based applications have developed rapidly, and cybersecurity has grown more critical. Inspired by Reinforcement Learning (RL) success in many domains, this paper proposes an Intrusion Detection System (IDS) to improve cybersecurity. The IDS based on two RL algorithms, i.e., Deep Q-Learning and Policy Gradient, is carefully formulated, strategically designed, and thoroughly evaluated at the packet-level and flow-level using the CICDDoS2019 dataset. Compared to other research work in a similar line of research, this paper is focused on providing a systematic and complete design paradigm of IDS based on RL algorithms, at both the packet and flow levels. For the packet-level RL-based IDS, first, the session data are transformed into images via an image embedding method proposed in this work. A comparison between 1D-Convolutional Neural Networks (1D-CNN) and CNN for extracting features from these images (for further RL agent training) is drawn from the quantitative results. In addition, an anomaly detection module is designed to detect unknown network traffic. For flow-level IDS, a Conditional Generative Adversarial Network (CGAN) and the (Formula presented.) -greedy strategy are adopted in designing the exploration module for RL agent training. To improve the robustness of the intrusion detection, a sample agent with a complement reward policy of the RL agent is introduced for the purpose of adversarial training. The experimental results of the proposed RL-based IDS show improved results over the state-of-the-art algorithms presented in the literature for packet-level and flow-level IDS.