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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)
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)
F. Crecchi, Bacciu, D., and 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, pp. 483-488. (552.39 KB)
D. Maiorca and Biggio, B., Digital Investigation of PDF Files: Unveiling Traces of Embedded Malware, IEEE Security and Privacy: Special Issue on Digital Forensics, vol. 17, no. 1, pp. 63-71, 2019. (838.95 KB)
L. Demetrio, Biggio, B., Lagorio, G., Roli, F., and 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., and 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, pp. 2565–2567.
M. Melis, Demontis, A., Pintor, M., Sotgiu, A., and Biggio, B., secml: A Python Library for Secure and Explainable Machine Learning. 2019. (1.1 MB)
D. Maiorca, Biggio, B., and Giacinto, G., Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks, ACM Computing Surveys, vol. 52, no. 4, 2019. (1.21 MB)
P. Temple, Acher, M., Perrouin, G., Biggio, B., Jezequel, J. - M., and 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, pp. 277–288. (2.09 MB)
A. Demontis, Melis, M., Pintor, M., Jagielski, M., Biggio, B., Oprea, A., Nita-Rotaru, C., and 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), p. 321--338. (1.09 MB)
A. Demontis, Melis, M., Biggio, B., Maiorca, D., Arp, D., Rieck, K., Corona, I., Giacinto, G., and Roli, F., Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection, IEEE Trans. Dependable and Secure Computing, vol. 16, no. 4, pp. 711-724, 2019. (3.61 MB)
B. Kolosnjaji, Demontis, A., Biggio, B., Maiorca, D., Giacinto, G., Eckert, C., and Roli, F., Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables, in 2018 26th European Signal Processing Conference (EUSIPCO), Rome, 2018, pp. 533-537. (674.62 KB)
M. Melis, Maiorca, D., Biggio, B., Giacinto, G., and Roli, F., Explaining Black-box Android Malware Detection, in 26th European Signal Processing Conference (EUSIPCO '18), Rome, Italy, 2018, pp. 524-528. (431.78 KB)
M. Jagielski, Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., and 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 and Roli, F., Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning, Pattern Recognition, vol. 84, pp. 317-331, 2018. (3.76 MB)
M. Melis, Demontis, A., Biggio, B., Brown, G., Fumera, G., and 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), pp. 751-759. (3.16 MB)
P. Piredda, Ariu, D., Biggio, B., Corona, I., Piras, L., Giacinto, G., and Roli, F., Deepsquatting: Learning-based Typosquatting Detection at Deeper Domain Levels, in 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017), 2017, vol. 10640 of LNCS, pp. 347-358. (1.21 MB)
I. Corona, Biggio, B., Contini, M., Piras, L., Corda, R., Mereu, M., Mureddu, G., Ariu, D., and Roli, F., DeltaPhish: Detecting Phishing Webpages in Compromised Websites, 22nd European Symposium on Research in Computer Security (ESORICS), vol. 10492. Springer International Publishing, Norway, September 11-15, 2017, pp. 370–388, 2017. (4.13 MB)
D. Maiorca, Russu, P., Corona, I., Biggio, B., and Giacinto, G., Detection of Malicious Scripting Code through Discriminant and Adversary-Aware API Analysis, in 1st Italian Conference on CyberSecurity (ITASEC), 2017, vol. 1816, pp. 96-105. (371.53 KB)
A. Demontis, Biggio, B., Fumera, G., Giacinto, G., and Roli, F., Infinity-norm Support Vector Machines against Adversarial Label Contamination, 1st Italian Conference on CyberSecurity (ITASEC). Venice, Italy , pp. 106-115, 2017. (504.93 KB)
S. Rota Bulò, Biggio, B., Pillai, I., Pelillo, M., and Roli, F., Randomized Prediction Games for Adversarial Machine Learning, IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 11, pp. 2466-2478, 2017. (1.52 MB) (256.21 KB)
B. Biggio, Fumera, G., Marcialis, G. L., and Roli, F., Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 3, pp. 561-575, 2017. (5.7 MB)
L. Muñoz-González, Biggio, B., Demontis, A., Paudice, A., Wongrassamee, V., Lupu, E. C., and Roli, F., Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization, in 10th ACM Workshop on Artificial Intelligence and Security, 2017, pp. 27-38. (4.08 MB)