Adversarial robustness of deep reinforcement learning-based intrusion detection

Machine learning techniques, including Deep Reinforcement Learning (DRL), enhance intrusion detection systems by adapting to new threats. However, DRL's reliance on vulnerable deep neural networks leads to susceptibility to adversarial examples-perturbations designed to evade detection. While adversarial examples are well-studied in deep learning, their impact on DRL-based intrusion detection remains underexplored, particularly in critical domains. This article conducts a thorough analysis of DRL-based intrusion detection's vulnerability to adversarial examples. It systematically evaluates key hyperparameters such as DRL algorithms, neural network depth, and width, impacting agents' robustness. The study extends to black-box attacks, demonstrating adversarial transferability across DRL algorithms. Findings emphasize neural network architecture's critical role in DRL agent robustness, addressing underfitting and overfitting challenges. Practical implications include insights for optimizing DRL-based intrusion detection agents to enhance performance and resilience. Experiments encompass multiple DRL algorithms tested on three datasets: NSL-KDD, UNSW-NB15, and CICIoV2024, against gradient-based adversarial attacks, with publicly available implementation code.