From the 1 of 4 linked papers with an AI index.
4 papers
VanillaBench: The Hidden Accuracy Cost of Adversarial Robustness
Niklas Bunzel
The paper presents VanillaBench, a benchmark that measures how much clean (vanilla) accuracy is lost when models are trained for adversarial robustness, revealing a larger accuracy…
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…
Quantifying the Risk of Transferred Black Box Attacks
Disesdi Susanna Cox, Niklas Bunzel
Neural networks have become pervasive across various applications, including security-related products. However, their widespread adoption has heightened concerns regarding vulnera…
The Relationship Between Network Similarity and Transferability of Adversarial Attacks
Gerrit Klause, Niklas Bunzel
Neural networks are vulnerable to adversarial attacks, and several defenses have been proposed. Designing a robust network is a challenging task given the wide range of attacks tha…