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cs.CR2026
Catastrophic Learning: A New Attack Vector on Continual Learning Networks
Benedikt Kluss, Niklas Bunzel
Continual Learning (CL) enables deep learning models to iteratively learn from a stream of data without forgetting prior knowledge. Existing adversarial research on CL primarily ai…
cs.CR2026
VanillaBench: The Hidden Accuracy Cost of Adversarial Robustness
Niklas Bunzel
Adversarial robustness research has produced hundreds of defended models over the past decade, yet the literature almost universally reports robustness results in isolation: standa…
cs.CR2026
Detecting Adversarial Evasion Attacks Against Autoencoder-Based Network Intrusion Detection Systems
Niklas Bunzel, Ashim Siwakoti
Evasion attacks deliberately manipulate input to an ML-based system to produce an incorrect prediction while the manipulated input still appears benign. The PANDA framework has dem…