Publications

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Thesis
B. Biggio, Adversarial Pattern Classification, University of Cagliari, Cagliari (Italy), 2010. (2.65 MB)
Magazine Article
B. Biggio, Fumera, G., Pillai, I., Roli, F., and Satta, R., Evading SpamAssassin with obfuscated text images, Virus Bulletin, no. 11-2007, 2007. (689 KB)
Journal Article
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. 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)
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)
B. Biggio, Fumera, G., Pillai, I., and Roli, F., A survey and experimental evaluation of image spam filtering techniques, Pattern Recognition Letters, vol. 32, pp. 1436 - 1446, 2011. (2.12 MB)
H. Xiao, Biggio, B., Nelson, B., Xiao, H., Eckert, C., and Roli, F., Support Vector Machines under Adversarial Label Contamination, Neurocomputing, Special Issue on Advances in Learning with Label Noise, vol. 160, pp. 53-62, 2015. (2.8 MB)
A. Demontis, Melis, M., Biggio, B., Fumera, G., and Roli, F., Super-sparse Learning in Similarity Spaces, IEEE Computational Intelligence Magazine, vol. 11, no. 4, pp. 36-45, 2016. (555.22 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)
B. Biggio, Fumera, G., and Roli, F., Security evaluation of pattern classifiers under attack, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 4, pp. 984-996, 2014. (1.35 MB)
B. Biggio, Akhtar, Z., Fumera, G., Marcialis, G. L., and Roli, F., Security evaluation of biometric authentication systems under real spoofing attacks, IET Biometrics, vol. 1, no. 1, pp. 11-24, 2012. (3.21 MB)
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., and Roli, F., Pattern Recognition Systems under Attack: Design Issues and Research Challenges, Int'l J. Patt. Recogn. Artif. Intell., vol. 28, no. 7, p. 1460002, 2014. (1.41 MB)
B. Biggio, Fumera, G., and Roli, F., Multiple Classifier Systems for Robust Classifier Design in Adversarial Environments, Journal of Machine Learning and Cybernetics, vol. 1, pp. 27–41, 2010. (844.91 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)
G. Ennas, Biggio, B., and Di Guardo, M. Chiara, Data-driven Journal Meta-ranking in Business and Management, Scientometrics, pp. 1-19, 2015. (896.37 KB)
F. Zhang, Chan, P. P. K., Biggio, B., Yeung, D. S., and Roli, F., Adversarial Feature Selection Against Evasion Attacks, IEEE Transactions on Cybernetics, vol. 46, no. 3, pp. 766-777, 2016. (2.12 MB)
B. Biggio, Fumera, G., Russu, P., Didaci, L., and Roli, F., Adversarial Biometric Recognition: A Review on Biometric System Security from the Adversarial Machine Learning Perspective, IEEE Signal Processing Magazine, vol. 32, no. 5, pp. 31-41, 2015. (751.08 KB)
Conference Proceedings
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.
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)
Conference Paper
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)
D. M. Freeman, Jain, S., Duermuth, M., Biggio, B., and Giacinto, G., Who Are You? A Statistical Approach to Measuring User Authenticity, in Proc. 23rd Annual Network & Distributed System Security Symposium (NDSS), 2016. (764.14 KB)
B. Nelson, Biggio, B., and Laskov, P., Understanding the Risk Factors of Learning in Adversarial Environments, in 4th ACM Workshop on Artificial Intelligence and Security (AISec 2011), Chicago, IL, USA, 2011, pp. 87–92. (132.42 KB)
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.

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