Publications
, “Machine Learning under Attack: Vulnerability Exploitation and Security Measures (Invited Keynote at IH&MMSec '16)”, in 4th ACM Workshop on Information Hiding & Multimedia Security, Vigo, Spain, 2016, pp. 1-2.
(138.98 KB)
(138.98 KB), “Secure Kernel Machines against Evasion Attacks”, in 9th ACM Workshop on Artificial Intelligence and Security, Vienna, Austria, 2016, pp. 59-69. (686.41 KB)
(686.41 KB), “On Security and Sparsity of Linear Classifiers for Adversarial Settings”, in Joint IAPR Int'l Workshop on Structural, Syntactic, and Statistical Pattern Recognition, Merida, Mexico, 2016, vol. 10029 of LNCS, pp. 322-332. (425.68 KB)
(425.68 KB), “Super-sparse Learning in Similarity Spaces”, IEEE Computational Intelligence Magazine, vol. 11, no. 4, pp. 36-45, 2016. (555.22 KB)
(555.22 KB), “Who Are You? A Statistical Approach to Measuring User Authenticity”, in Proc. 23rd Annual Network & Distributed System Security Symposium (NDSS), 2016. (764.14 KB)
(764.14 KB), “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)
(3.16 MB), “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)
(1.21 MB), “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)
(4.13 MB), “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)
(371.53 KB), “Infinity-norm Support Vector Machines against Adversarial Label Contamination”, 1st Italian Conference on CyberSecurity (ITASEC). Venice, Italy , pp. 106-115, 2017. (504.93 KB)
(504.93 KB), “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)
(1.52 MB) (256.21 KB), “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)
(5.7 MB), “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)
(4.08 MB), “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)
(674.62 KB), “Explaining Black-box Android Malware Detection”, in 26th European Signal Processing Conference (EUSIPCO '18), Rome, Italy, 2018, pp. 524-528. (431.78 KB)
(431.78 KB), “Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning”, in 39th IEEE Symposium on Security and Privacy, 2018. (1.02 MB)
(1.02 MB), “Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning”, Pattern Recognition, vol. 84, pp. 317-331, 2018. (3.76 MB)
(3.76 MB), “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)
(552.39 KB), “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)
(838.95 KB), “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)
(801.85 KB), “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.
, “Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks”, ACM Computing Surveys, vol. 52, no. 4, 2019. (1.21 MB)
(1.21 MB), “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)
(2.09 MB), “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)
(1.09 MB), “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)
(3.61 MB)
