3 papers
cs.CR2025
How stealthy is stealthy? Studying the Efficacy of Black-Box Adversarial Attacks in the Real World
Francesco Panebianco, Mario D'Onghia, Stefano Zanero aand Michele Carminati
Deep learning systems, critical in domains like autonomous vehicles, are vulnerable to adversarial examples (crafted inputs designed to mislead classifiers). This study investigate…
cs.CR2025
Tarallo: Evading Behavioral Malware Detectors in the Problem Space
Gabriele Digregorio, Salvatore Maccarrone, Mario D'Onghia +4
Machine learning algorithms can effectively classify malware through dynamic behavior but are susceptible to adversarial attacks. Existing attacks, however, often fail to find an e…
cs.CR2025
PackHero: A Scalable Graph-based Approach for Efficient Packer Identification
Marco Di Gennaro, Mario D'Onghia, Mario Polino +2
Anti-analysis techniques, particularly packing, challenge malware analysts, making packer identification fundamental. Existing packer identifiers have significant limitations: sign…