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 | 1409 | 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 | 613 | 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 | 398 | 2016 |
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 | 356 | 2009 |
Dynamic classifier selection based on multiple classifier behaviour G Giacinto, F Roli Pattern Recognition 34 (9), 1879-1882, 2001 | 336 | 2001 |
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 | 333 | 2018 |
Fusion of multiple classifiers for intrusion detection in computer networks G Giacinto, F Roli, L Didaci Pattern recognition letters 24 (12), 1795-1803, 2003 | 296 | 2003 |
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 | 291 | 2017 |
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 | 281 | 2008 |
Methods for designing multiple classifier systems F Roli, G Giacinto, G Vernazza Multiple Classifier Systems: Second International Workshop, MCS 2001 …, 2001 | 280 | 2001 |
An approach to the automatic design of multiple classifier systems G Giacinto, F Roli Pattern recognition letters 22 (1), 25-33, 2001 | 275 | 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 | 248 | 2017 |
Reject option with multiple thresholds G Fumera, F Roli, G Giacinto Pattern recognition 33 (12), 2099-2101, 2000 | 239 | 2000 |
Adversarial attacks against intrusion detection systems: Taxonomy, solutions and open issues I Corona, G Giacinto, F Roli Information Sciences 239, 201-225, 2013 | 232 | 2013 |
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 | 190 | 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 | 189 | 2000 |
Alarm clustering for intrusion detection systems in computer networks R Perdisci, G Giacinto, F Roli Engineering Applications of Artificial Intelligence 19 (4), 429-438, 2006 | 163 | 2006 |
HMMPayl: An intrusion detection system based on Hidden Markov Models D Ariu, R Tronci, G Giacinto computers & security 30 (4), 221-241, 2011 | 162 | 2011 |
Methods for dynamic classifier selection G Giacinto, F Roli Proceedings 10th international conference on image analysis and processing …, 1999 | 160 | 1999 |
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 | 158 | 2005 |