Publications

Export 460 results:
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. Meloni, Sanna, A., Maiorca, D., e Giacinto, G., «Extended Abstract: Effective Call Graph Fingerprinting for the Analysis and Classification of Windows Malware», 19th Conference on Detection of Intrusions and Malware & Vulnerability Assessment (DIMVA). pagg 42-52, 2022. (328.32 KB)
F. Crecchi, Melis, M., Sotgiu, A., Bacciu, D., e Biggio, B., «FADER: Fast adversarial example rejection», Neurocomputing, vol 470, pagg 257-268, 2022.
A. Janovsky, Maiorca, D., Marko, D., Matyas, V., e Giacinto, G., «A Longitudinal Study of Cryptographic API: A Decade of Android Malware», 19th International Conference on Security and Cryptography (SECRYPT). pagg 121-133, 2022. (251.06 KB)
L. Borzacchiello, Coppa, E., Maiorca, D., Columbu, A., Demetrescu, C., e Giacinto, G., «Reach Me if You Can: On Native Vulnerability Reachability in Android Apps», 27th European Symposium on Research in Computer Security (ESORICS). 2022. (979.51 KB)
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.
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.
L. Putzu, Untesco, M., e Fumera, G., «Automatic Myelofibrosis Grading from Silver-Stained Images», in Computer Analysis of Images and Patterns, Cham, 2021, pagg 195–205.
A. Loddo e Putzu, L., «On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario», AI, vol 2, pagg 394–412, 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.
E. Ledda, Putzu, L., Delussu, R., Loddo, A., e Fumera, G., «How Realistic Should Synthetic Images Be for Training Crowd Counting Models?», in Computer Analysis of Images and Patterns, Cham, 2021, pagg 46–56.
L. Putzu, Loddo, A., e Di Ruberto, C., «Invariant Moments, Textural and Deep Features for Diagnostic MR and CT Image Retrieval», in Computer Analysis of Images and Patterns, Cham, 2021, pagg 287–297.
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.
G. Malandrone, Virdis, G., Maiorca, D., e Giacinto, G., «PowerDecode: A PowerShell Script Decoder Dedicated to Malware Analysis», 5th Italian Conference on CyberSecurity (ITASEC). 2021. (982.03 KB)

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