| Evasion attacks against machine learning at test time B Biggio, I Corona, D Maiorca, B Nelson, N Šrndić, P Laskov, G Giacinto, ... Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013 | 1887 | 2013 |
| Design of effective neural network ensembles for image classification purposes G Giacinto, F Roli Image and Vision Computing 19 (9-10), 699-707, 2001 | 632 | 2001 |
| Novel feature extraction, selection and fusion for effective malware family classification M Ahmadi, D Ulyanov, S Semenov, M Trofimov, G Giacinto Proceedings of the sixth ACM conference on data and application security and …, 2016 | 495 | 2016 |
| Adversarial malware binaries: Evading deep learning for malware detection in executables B Kolosnjaji, A Demontis, B Biggio, D Maiorca, G Giacinto, C Eckert, ... 2018 26th European signal processing conference (EUSIPCO), 533-537, 2018 | 459 | 2018 |
| McPAD: A multiple classifier system for accurate payload-based anomaly detection R Perdisci, D Ariu, P Fogla, G Giacinto, W Lee Computer networks 53 (6), 864-881, 2009 | 369 | 2009 |
| Yes, machine learning can be more secure! a case study on android malware detection A Demontis, M Melis, B Biggio, D Maiorca, D Arp, K Rieck, I Corona, ... IEEE transactions on dependable and secure computing 16 (4), 711-724, 2017 | 368 | 2017 |
| Dynamic classifier selection based on multiple classifier behaviour G Giacinto, F Roli Pattern Recognition 34 (9), 1879-1881, 2001 | 361 | 2001 |
| Droidsieve: Fast and accurate classification of obfuscated android malware G Suarez-Tangil, SK Dash, M Ahmadi, J Kinder, G Giacinto, L Cavallaro Proceedings of the seventh ACM on conference on data and application …, 2017 | 319 | 2017 |
| Fusion of multiple classifiers for intrusion detection in computer networks G Giacinto, F Roli, L Didaci Pattern recognition letters 24 (12), 1795-1803, 2003 | 310 | 2003 |
| Intrusion detection in computer networks by a modular ensemble of one-class classifiers G Giacinto, R Perdisci, M Del Rio, F Roli Information Fusion 9 (1), 69-82, 2008 | 293 | 2008 |
| Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues I Corona, G Giacinto, F Roli Information sciences 239, 201-225, 2013 | 290 | 2013 |
| Methods for designing multiple classifier systems F Roli, G Giacinto, G Vernazza International Workshop on Multiple Classifier Systems, 78-87, 2001 | 286 | 2001 |
| An approach to the automatic design of multiple classifier systems G Giacinto, F Roli Pattern recognition letters 22 (1), 25-33, 2001 | 280 | 2001 |
| Reject option with multiple thresholds G Fumera, F Roli, G Giacinto Pattern recognition 33 (12), 2099-2101, 2000 | 256 | 2000 |
| Stealth attacks: An extended insight into the obfuscation effects on android malware D Maiorca, D Ariu, I Corona, M Aresu, G Giacinto Computers & Security 51, 16-31, 2015 | 229 | 2015 |
| Combination of neural and statistical algorithms for supervised classification of remote-sensing images G Giacinto, F Roli, L Bruzzone Pattern Recognition Letters 21 (5), 385-397, 2000 | 194 | 2000 |
| Poisoning behavioral malware clustering B Biggio, K Rieck, D Ariu, C Wressnegger, I Corona, G Giacinto, F Roli Proceedings of the 2014 workshop on artificial intelligent and security …, 2014 | 176 | 2014 |
| A study on the performances of dynamic classifier selection based on local accuracy estimation L Didaci, G Giacinto, F Roli, GL Marcialis Pattern recognition 38 (11), 2188-2191, 2005 | 169 | 2005 |
| HMMPayl: An intrusion detection system based on Hidden Markov Models D Ariu, R Tronci, G Giacinto computers & security 30 (4), 221-241, 2011 | 168 | 2011 |
| Information fusion in content based image retrieval: A comprehensive overview L Piras, G Giacinto Information Fusion 37, 50-60, 2017 | 167 | 2017 |
