11 citations · 17 across the 6 of their papers we have counts for
5 papers
Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors
Biagio Montaruli, Luca Demetrio, Maura Pintor +3
Machine-learning phishing webpage detectors (ML-PWD) have been shown to suffer from adversarial manipulations of the HTML code of the input webpage. Nevertheless, the attacks recen…
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization
Giuseppe Floris, Raffaele Mura, Luca Scionis +4
Evaluating the adversarial robustness of machine learning models using gradient-based attacks is challenging. In this work, we show that hyperparameter optimization can improve fas…
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks
Giorgio Piras, Maura Pintor, Ambra Demontis +1
Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks…
Explaining Machine Learning DGA Detectors from DNS Traffic Data
Giorgio Piras, Maura Pintor, Luca Demetrio +1
One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of…
Robust Machine Learning for Malware Detection over Time
Daniele Angioni, Luca Demetrio, Maura Pintor +1
The presence and persistence of Android malware is an on-going threat that plagues this information era, and machine learning technologies are now extensively used to deploy more e…