11 citations · 11 across the 1 of their papers we have counts for
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Assessing the Impact of Packing on Machine Learning-Based Malware Detection and Classification Systems
Daniel Gibert, Nikolaos Totosis, Constantinos Patsakis +2
The proliferation of malware, particularly through the use of packing, presents a significant challenge to static analysis and signature-based malware detection techniques. The app…
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing
Daniel Gibert, Luca Demetrio, Giulio Zizzo +3
Deep learning-based malware detection systems are vulnerable to adversarial EXEmples - carefully-crafted malicious programs that evade detection with minimal perturbation. As such,…
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing
Daniel Gibert, Giulio Zizzo, Quan Le +1
Deep learning-based malware detectors have been shown to be susceptible to adversarial malware examples, i.e. malware examples that have been deliberately manipulated in order to a…