Adapting Under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security

Abstract

Evolving attacks are a critical challenge for the long-term success of Network Intrusion Detection Systems (NIDS). The rise of these changing patterns has exposed the limitations of traditional network security methods. While signature-based methods are used to detect different types of attacks, they often fail to detect unknown attacks. Moreover, the system requires frequent updates with new signatures as the attackers are constantly changing their tactics. In this paper, we design an environment where two agents improve their policies over time. The adversarial agent, referred to as the red agent, perturbs packets to evade the intrusion detection mechanism, whereas the blue agent learns new defensive policies using drift adaptation techniques to counter the attacks. Both agents adapt iteratively: the red agent responds to the evolving NIDS, while the blue agent adjusts to emerging attack patterns. By studying the model's learned policy, we offer concrete insights into drift adaptation techniques with high utility. Experiments show that the blue agent boosts model accuracy by 30% with just 2 to 3 adaptation steps using only 25 to 30 samples each.