Abstract
Recent work on network attacks have demonstrated that ML-based network intrusion detection systems (NIDS) can be evaded with adversarial perturbations. However, these attacks rely on complex optimizations that have large computational overheads, making them impractical in many real-world settings. In this paper, we introduce a lightweight adversarial agent that implements strategies (policies) trained via reinforcement learning (RL) that learn to evade ML-based NIDS without requiring online optimization. This attack proceeds by (1) offline training, where the agent learns to evade a surrogate ML model by perturbing malicious flows using network traffic data assumed to be collected via reconnaissance, then (2) deployment, where the trained agent is used in a compromised device controlled by an attacker to evade ML-based NIDS using learned attack strategies. We evaluate our approach across diverse NIDS and several white-, gray-, and black-box threat models. We demonstrate that attacks us