Publications

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2020
D. Maiorca, Demontis, A., Biggio, B., Roli, F., e Giacinto, G., «Adversarial Detection of Flash Malware: Limitations and Open Issues», Computers & Security, vol 96, 2020. (1.08 MB)
A. Sotgiu, Demontis, A., Melis, M., Biggio, B., Fumera, G., Feng, X., e Roli, F., «Deep Neural Rejection against Adversarial Examples», EURASIP Journal on Information Security, vol 5, 2020.
2021
L. Demetrio, Coull, S. E., Biggio, B., Lagorio, G., Armando, A., e Roli, F., «Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection», ACM Trans. Priv. Secur., vol 24, 2021.
H. - Y. Lin e Biggio, B., «Adversarial Machine Learning: Attacks From Laboratories to the Real World», Computer, vol 54, pagg 56-60, 2021.
P. Temple, Perrouin, G., Acher, M., Biggio, B., Jézéquel, J. - M., e Roli, F., «Empirical Assessment of Generating Adversarial Configurations for Software Product Lines», Empirical Software Engineering, vol 26, n° 6, 2021. (1.29 MB)
M. Pintor, Roli, F., Brendel, W., e Biggio, B., «Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints», in NeurIPS, 2021.
L. Demetrio, Biggio, B., Lagorio, G., Roli, F., e Armando, A., «Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware», IEEE Transactions on Information Forensics and Security, vol 16, pagg 3469-3478, 2021.
A. Emanuele Cinà, Vascon, S., Demontis, A., Biggio, B., Roli, F., e Pelillo, M., «The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?», in International Joint Conference on Neural Networks, (IJCNN) 2021, Shenzhen, China, 2021, pagg 1–8.
D. Solans, Biggio, B., e Castillo, C., «Poisoning Attacks on Algorithmic Fairness», in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020), 2021, pag 162--177. (1.05 MB)
M. Kravchik, Biggio, B., e Shabtai, A., «Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems», in Proceedings of the 36th Annual ACM Symposium on Applied Computing, New York, NY, USA, 2021, pagg 116–125.
2022
M. Melis, Scalas, M., Demontis, A., Maiorca, D., Biggio, B., Giacinto, G., e Roli, F., «Do Gradient-Based Explanations Tell Anything About Adversarial Robustness to Android Malware?», International Journal of Machine Learning and Cybernetics, vol 13, pagg 217-232, 2022. (1.2 MB)
A. Sotgiu, Pintor, M., e Biggio, B., «Explainability-Based Debugging of Machine Learning for Vulnerability Discovery», in Proc. 17th International Conference on Availability, Reliability and Security, New York, NY, USA, 2022.
F. Crecchi, Melis, M., Sotgiu, A., Bacciu, D., e Biggio, B., «FADER: Fast adversarial example rejection», Neurocomputing, vol 470, pagg 257-268, 2022.
M. Pintor, Demetrio, L., Sotgiu, A., Melis, M., Demontis, A., e Biggio, B., «secml: A Python Library for Secure and Explainable Machine Learning», SoftwareX, 2022.
L. Oneto, Navarin, N., Biggio, B., Errica, F., Micheli, A., Scarselli, F., Bianchini, M., Demetrio, L., Bongini, P., Tacchella, A., e Sperduti, A., «Towards learning trustworthily, automatically, and with guarantees on graphs: An overview», Neurocomputing, vol 493, pagg 217-243, 2022.

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