Publications
, “Adversarial Pattern Classification”, University of Cagliari, Cagliari (Italy), 2010.
(2.65 MB)
(2.65 MB), “Security of pattern recognition systems in adversarial environments”. 2012. (235.41 KB)
(235.41 KB), “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), “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), “Towards learning trustworthily, automatically, and with guarantees on graphs: An overview”, Neurocomputing, vol. 493, pp. 217-243, 2022.
, “Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks”, ACM Computing Surveys, vol. 52, no. 4, 2019. (1.21 MB)
(1.21 MB), “A survey and experimental evaluation of image spam filtering techniques”, Pattern Recognition Letters, vol. 32, pp. 1436 - 1446, 2011. (2.12 MB)
(2.12 MB), “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)
(2.8 MB), “Super-sparse Learning in Similarity Spaces”, IEEE Computational Intelligence Magazine, vol. 11, no. 4, pp. 36-45, 2016. (555.22 KB)
(555.22 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), “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)
(1.35 MB), “Security evaluation of biometric authentication systems under real spoofing attacks”, IET Biometrics, vol. 1, no. 1, pp. 11-24, 2012. (3.21 MB)
(3.21 MB), “secml: A Python Library for Secure and Explainable Machine Learning”, SoftwareX, 2022.
, “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), “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)
(1.41 MB), “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)
(844.91 KB), “Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware”, IEEE Transactions on Information Forensics and Security, vol. 16, pp. 3469-3478, 2021.
, “FADER: Fast adversarial example rejection”, Neurocomputing, vol. 470, pp. 257-268, 2022.
, “Empirical Assessment of Generating Adversarial Configurations for Software Product Lines”, Empirical Software Engineering, vol. 26, no. 6, 2021. (1.29 MB)
(1.29 MB), “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)
(1.2 MB), “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), “Deep Neural Rejection against Adversarial Examples”, EURASIP Journal on Information Security, vol. 5, 2020.
, “Data-driven Journal Meta-ranking in Business and Management”, Scientometrics, pp. 1-19, 2015. (896.37 KB)
(896.37 KB), “Adversarial Machine Learning: Attacks From Laboratories to the Real World”, Computer, vol. 54, pp. 56-60, 2021.
