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Concept drift mitigation through community and spectral graph analysis for the detection of cyberattacks in network traffic

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

In network traffic, legitimate behaviours and attack techniques evolve jointly - the phenomenon known as 'concept drift' [1]. Every detector is thereby left obsolete between two updates, and always one step behind adversaries. In this work, we propose to move the point of intervention from the model, repaired after the drift, to the feature space, selected before learning. We therefore introduce t-robustness, a stability score defined for each feature independently of any detection model, comparable across an entire feature space. It combines the step-by-step distance between successive statis

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