Explaining Machine Learning DGA Detectors from DNS Traffic Data. - Giorgio Piras; Maura Pintor; Luca Demetrio; Battista Biggio (ITASEC, 2022)

Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization. - Giorgio Piras; Giuseppe Floris; Raffaele Mura; Luca Scionis; Maura Pintor; Battista Biggio; Ambra Demontis (ESANN, 2023)

Adversarial Attacks Against Uncertainty Quantification. - Emanuele Ledda; Daniele Angioni; Giorgio Piras; Giorgio Fumera; Battista Biggio; Fabio Roli (ICCV (Workshops), 2023)

Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks. - Giorgio Piras; Maura Pintor; Ambra Demontis; Battista Biggio (ICMLC, 2023)

AI Security and Safety: The PRALab Research Experience. - Ambra Demontis; Maura Pintor; Luca Demetrio; Angelo Sotgiu; Daniele Angioni; Giorgio Piras; Srishti Gupta; Battista Biggio; Fabio Roli (Ital-IA, 2023)

On the Robustness of Adversarial Training Against Uncertainty Attacks. - Emanuele Ledda; Giovanni Scodeller; Daniele Angioni; Giorgio Piras; Antonio Emanuele Cinà; Giorgio Fumera; Battista Biggio; Fabio Roli (CoRR, 2024)

An Experimental Analysis of Semi-supervised Learning for Malware Detection. - Luca Minnei; Giorgio Piras; Angelo Sotgiu; Maura Pintor; Ambra Demontis; Davide Maiorca; Battista Biggio (ITASEC/SERICS, 2025)

HO-FMN: Hyperparameter optimization for fast minimum-norm attacks. - Raffaele Mura; Giuseppe Floris; Luca Scionis; Giorgio Piras; Maura Pintor; Ambra Demontis; Giorgio Giacinto; Battista Biggio; Fabio Roli (Neurocomputing, 2025)

Adversarial pruning: A survey and benchmark of pruning methods for adversarial robustness. - Giorgio Piras; Maura Pintor; Ambra Demontis; Battista Biggio; Giorgio Giacinto; Fabio Roli (Pattern Recognit., 2025)

Adversarial pruning: improving evaluations and methods. - Giorgio Piras (2025)

Regression-aware Continual Learning for Android Malware Detection. - Daniele Ghiani; Daniele Angioni; Giorgio Piras; Angelo Sotgiu; Luca Minnei; Srishti Gupta; Maura Pintor; Fabio Roli; Battista Biggio (CoRR, 2025)