Publications

Export 90 results:
Filters: Author is Battista Biggio  [Clear All Filters]
2020
D. Maiorca, Demontis, A., Biggio, B., Roli, F., and 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., and 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., and 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 and Biggio, B., “Adversarial Machine Learning: Attacks From Laboratories to the Real World”, Computer, vol. 54, pp. 56-60, 2021.
P. Temple, Perrouin, G., Acher, M., Biggio, B., Jézéquel, J. - M., and Roli, F., “Empirical Assessment of Generating Adversarial Configurations for Software Product Lines”, Empirical Software Engineering, vol. 26, no. 6, 2021. (1.29 MB)
M. Pintor, Roli, F., Brendel, W., and Biggio, B., “Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints”, in NeurIPS, 2021.
L. Demetrio, Biggio, B., Lagorio, G., Roli, F., and Armando, A., “Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware”, IEEE Transactions on Information Forensics and Security, vol. 16, pp. 3469-3478, 2021.
A. Emanuele Cinà, Vascon, S., Demontis, A., Biggio, B., Roli, F., and 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, pp. 1–8.
D. Solans, Biggio, B., and Castillo, C., “Poisoning Attacks on Algorithmic Fairness”, in Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020), 2021, p. 162--177. (1.05 MB)
M. Kravchik, Biggio, B., and 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, pp. 116–125.
2022
M. Melis, Scalas, M., Demontis, A., Maiorca, D., Biggio, B., Giacinto, G., and Roli, F., “Do Gradient-Based Explanations Tell Anything About Adversarial Robustness to Android Malware?”, International Journal of Machine Learning and Cybernetics, vol. 13, pp. 217-232, 2022. (1.2 MB)
A. Sotgiu, Pintor, M., and 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., and Biggio, B., “FADER: Fast adversarial example rejection”, Neurocomputing, vol. 470, pp. 257-268, 2022.
M. Pintor, Demetrio, L., Sotgiu, A., Melis, M., Demontis, A., and 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., and Sperduti, A., “Towards learning trustworthily, automatically, and with guarantees on graphs: An overview”, Neurocomputing, vol. 493, pp. 217-243, 2022.

Pages