Adaptive active-defense hardening of ML-based NIDS against RL-driven adversaries: A comparative analysis with static defenses

Summary

The paper compares adaptive active-defense methods for hardening ML-based network intrusion detection systems against reinforcement-learning-driven adversaries with static defenses. It was published in the Journal of Information Security and Applications, Volume 100, in July 2026, by Iacovos Ioannou, Christophoros Christophorou, Andreas Andreou, Marios Raspopoulos, Constandinos Mavromoustakis, Vasos Vassiliou and Fabrizio Granelli.

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