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Network intrusion detectors can survive non-stationary drift without model updates by filtering for feature-level temporal divergence rather than reacting with post-hoc retraining.
Transforming raw traffic into dynamic graphs reveals hidden vulnerabilities in water distribution networks, significantly enhancing cyberattack detection.