Author

Daniel Gibert

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Open access May 2024

Certified Adversarial Robustness of End-to-End Malware Detectors via (De)Randomized Smoothing

A novel deterministic certification schema based on (de)randomized smoothing that guarantees that each chunk either contains or does not contain an adversarial perturbation, enabling it to handle manipulations occurring at arbitrary locations within the program and compute deterministic estimates of the perturbation magnitude required to evade detection.

Daniel Gibert, Luca Demetrio, Giulio Zizzo et al. · ACM Transactions on Privacy... · 4 citations

Jul 2026

EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability

ExE-Bench assesses performance, temporal and adversarial robustness, and computational overhead, aggregating them into a single score for direct and fair model comparison, and highlights how evaluations conducted only after deployment are suboptimal and unable to provide a complete picture of their performance.

Andrea Ponte, Daniel Gibert, M. Kozák et al. · arXiv.org · 0 citations