Publications

Export 460 results:
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. Meloni, Sanna, A., Maiorca, D., and 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). pp. 42-52, 2022. (328.32 KB)
F. Crecchi, Melis, M., Sotgiu, A., Bacciu, D., and Biggio, B., FADER: Fast adversarial example rejection, Neurocomputing, vol. 470, pp. 257-268, 2022.
A. Janovsky, Maiorca, D., Marko, D., Matyas, V., and Giacinto, G., A Longitudinal Study of Cryptographic API: A Decade of Android Malware, 19th International Conference on Security and Cryptography (SECRYPT). pp. 121-133, 2022. (251.06 KB)
L. Borzacchiello, Coppa, E., Maiorca, D., Columbu, A., Demetrescu, C., and 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., 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.
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.
L. Putzu, Untesco, M., and Fumera, G., Automatic Myelofibrosis Grading from Silver-Stained Images, in Computer Analysis of Images and Patterns, Cham, 2021, pp. 195–205.
A. Loddo and Putzu, L., On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario, AI, vol. 2, pp. 394–412, 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.
E. Ledda, Putzu, L., Delussu, R., Loddo, A., and Fumera, G., How Realistic Should Synthetic Images Be for Training Crowd Counting Models?, in Computer Analysis of Images and Patterns, Cham, 2021, pp. 46–56.
L. Putzu, Loddo, A., and 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, pp. 287–297.
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.
G. Malandrone, Virdis, G., Maiorca, D., and 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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