Publications

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A
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)
D. Maiorca, Demontis, A., Biggio, B., Roli, F., and Giacinto, G., “Adversarial Detection of Flash Malware: Limitations and Open Issues”, Computers & Security, vol. 96, 2020. (1.08 MB)
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.
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)
H. - Y. Lin and Biggio, B., “Adversarial Machine Learning: Attacks From Laboratories to the Real World”, Computer, vol. 54, pp. 56-60, 2021.
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)
B. Biggio, “Adversarial Pattern Classification”, University of Cagliari, Cagliari (Italy), 2010. (2.65 MB)
B. Biggio, Fumera, G., and Roli, F., “Adversarial Pattern Classification Using Multiple Classifiers and Randomisation”, in 12th Joint IAPR International Workshop on Structural and Syntactic Pattern Recognition (SSPR 2008), Orlando, Florida, USA, 2008. (395.38 KB)
G. L. Marcialis, Fumera, G., and Biggio, B., “Anti-spoofing: Multimodal”, in Encyclopedia of Biometrics, S. Z. Li and Jain, A. K. Springer US, 2014, pp. 1-4.
B
B. Biggio, Corona, I., Fumera, G., Giacinto, G., and Roli, F., “Bagging classifiers for fighting poisoning attacks in adversarial classification tasks”, in Multiple Classifier Systems (MCS 2011), 2011, vol. 6713, pp. 350-359. (231.43 KB)
B. Biggio, Fumera, G., and Roli, F., “Bayesian Analysis of Linear Combiners”, in 7th Int. Workshop on Multiple Classifier Systems (MCS 2007), Prague, Czech Republic, 2007, vol. 4472, pp. 292-301. (149.24 KB)
B. Biggio, Fumera, G., and Roli, F., “Bayesian Linear Combination of Neural Networks”, in Innovations in Neural Information Paradigms and Applications, vol. 247, M. Bianchini, Maggini, M., Scarselli, F., and Jain, L. C. Springer Berlin / Heidelberg, 2009, pp. 201-230. (435.32 KB)
D
B. Biggio, Pillai, I., Rota Bulò, S., Ariu, D., Pelillo, M., and Roli, F., “Is Data Clustering in Adversarial Settings Secure?”, in AISec'13: Proceedings of the 2013 ACM Workshop on Artificial Intelligence and Security, Berlin, 2013, pp. 87-98. (300.52 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)
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)
A. Sotgiu, Demontis, A., Melis, M., Biggio, B., Fumera, G., Feng, X., and Roli, F., “Deep Neural Rejection against Adversarial Examples”, EURASIP Journal on Information Security, vol. 5, 2020.
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)
B. Biggio, Fumera, G., and Roli, F., “Design of Robust Classifiers for Adversarial Environments”, in IEEE International Conference on Systems, Man, and Cybernetics (SMC 2011), Anchorage, Alaska, USA, 2011, pp. 977–982. (328.68 KB)
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)
M. Ahmadi, Biggio, B., Arzt, S., Ariu, D., and Giacinto, G., “Detecting Misuse of Google Cloud Messaging in Android Badware”, in 6th Workshop on Security and Privacy in Smartphones and Mobile Devices (SPSM), Vienna, Austria, 2016, pp. 103-112. (626.38 KB)
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)
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)
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)
E
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)

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