Publications

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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.
2019
F. Crecchi, Bacciu, D., e Biggio, B., «Detecting Adversarial Examples through Nonlinear Dimensionality Reduction», in 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning - ESANN '19, 2019, pagg 483-488. (552.39 KB)
D. Maiorca e Biggio, B., «Digital Investigation of PDF Files: Unveiling Traces of Embedded Malware», IEEE Security and Privacy: Special Issue on Digital Forensics, vol 17, n° 1, pagg 63-71, 2019. (838.95 KB)
L. Demetrio, Biggio, B., Lagorio, G., Roli, F., e Armando, A., «Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries», in 3rd Italian Conference on Cyber Security, ITASEC 2019, Pisa, Italy, 2019, vol 2315. (801.85 KB)
R. Labaca-Castro, Biggio, B., e Rodosek, G. Dreo, «Poster: Attacking Malware Classifiers by Crafting Gradient-Attacks That Preserve Functionality», in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, New York, NY, USA, 2019, pagg 2565–2567.
M. Melis, Demontis, A., Pintor, M., Sotgiu, A., e Biggio, B., «secml: A Python Library for Secure and Explainable Machine Learning». 2019. (1.1 MB)
D. Maiorca, Biggio, B., e Giacinto, G., «Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks», ACM Computing Surveys, vol 52, n° 4, 2019. (1.21 MB)
P. Temple, Acher, M., Perrouin, G., Biggio, B., Jezequel, J. - M., e Roli, F., «Towards Quality Assurance of Software Product Lines with Adversarial Configurations», in Proceedings of the 23rd International Systems and Software Product Line Conference - Volume A, New York, NY, USA, 2019, pagg 277–288. (2.09 MB)
A. Demontis, Melis, M., Pintor, M., Jagielski, M., Biggio, B., Oprea, A., Nita-Rotaru, C., e Roli, F., «Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks», in 28th Usenix Security Symposium, Santa Clara, California, USA, 2019, vol 28th {USENIX} Security Symposium ({USENIX} Security 19), pag 321--338. (1.09 MB)
A. Demontis, Melis, M., Biggio, B., Maiorca, D., Arp, D., Rieck, K., Corona, I., Giacinto, G., e Roli, F., «Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection», IEEE Trans. Dependable and Secure Computing, vol 16, n° 4, pagg 711-724, 2019. (3.61 MB)
2018
B. Kolosnjaji, Demontis, A., Biggio, B., Maiorca, D., Giacinto, G., Eckert, C., e Roli, F., «Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables», in 2018 26th European Signal Processing Conference (EUSIPCO), Rome, 2018, pagg 533-537. (674.62 KB)
M. Melis, Maiorca, D., Biggio, B., Giacinto, G., e Roli, F., «Explaining Black-box Android Malware Detection», in 26th European Signal Processing Conference (EUSIPCO '18), Rome, Italy, 2018, pagg 524-528. (431.78 KB)
M. Jagielski, Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., e Li, B., «Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning», in 39th IEEE Symposium on Security and Privacy, 2018. (1.02 MB)
B. Biggio e Roli, F., «Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning», Pattern Recognition, vol 84, pagg 317-331, 2018. (3.76 MB)
2017
M. Melis, Demontis, A., Biggio, B., Brown, G., Fumera, G., e Roli, F., «Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid», in ICCV 2017 Workshop on Vision in Practice on Autonomous Robots (ViPAR), Venice, Italy, 2017, vol 2017 IEEE International Conference on Computer Vision Workshops (ICCVW), pagg 751-759. (3.16 MB)

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