Publications

Export 414 results:
Submitted
M. Scalas, Maiorca, D., Mercaldo, F., Visaggio, C. Aaron, Martinelli, F., and Giacinto, G., “R-PackDroid: Practical On-Device Detection of Android Ransomware”, ArXiv Preprint, Submitted. (986.09 KB)
In Press
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 26th European Signal Processing Conference (EUSIPCO '18), In Press. (726.7 KB)
A. Carcangiu, Spano, L. Davide, Fumera, G., and Roli, F., “DEICTIC: a Compositional and Declarative Gesture Description based on Hidden Markov Model”, International Journal of Human-Computer Studies, In Press.
D. Maiorca and Biggio, B., “Digital Investigation of PDF Files: Unveiling Traces of Embedded Malware”, IEEE Security and Privacy: Special Issue on Digital Forensics, In Press. (838.95 KB)
W. W. Y. Ng, Hu, J., Yeung, D., Yin, S., and Roli, F., “Diversified Sensitivity based Undersampling for Imbalance Classification Problems”, IEEE Transactions on Cybernetics, In Press. (1.91 MB)
G. Suarez-Tangil, Dash, S. Kumar, Ahmadi, M., Kinder, J., Giacinto, G., and Cavallaro, L., “DroidSieve: Fast and Accurate Classification of Obfuscated Android Malware”, in Proceedings of the Seventh {ACM} Conference on Data and Application Security and Privacy, {CODASPY} 2017, In Press. (478.59 KB)
M. Melis, Maiorca, D., Biggio, B., Giacinto, G., and Roli, F., “Explaining Black-box Android Malware Detection”, in 26th European Signal Processing Conference (EUSIPCO '18), In Press. (431.78 KB)
R. Soleymani, Granger, E., and Fumera, G., “Loss Factors for Learning Boosting Ensembles from Imbalanced Data”, in 23rd International Conference on Pattern Recognition (ICPR 2016), Cancún, Mexico, In Press.
B. Lavi, Fumera, G., and Roli, F., “A Multi-Stage Ranking Approach for Fast Person Re-Identification”, IET Computer Vision, In Press. (1.07 MB)
Y. Guan, Li, C. - T., and Roli, F., “On Reducing the Effect of Covariate Factors in Gait Recognition: a Classifier Ensemble Method”, IEEE Transactions on Pattern Analysis and Machine Intelligence, In Press. (311.43 KB) (151.4 KB)
M. Ahmed, Didaci, L., Fumera, G., Roli, F., and Lavi, B., “Using Diversity for Classifier Ensemble Pruning: An Empirical Investigation”, Theoretical and Applied Informatics, In Press.
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, In Press. (3.61 MB)
2018
R. Soleymani, Granger, E., and Fumera, G., “F-Measure Curves for Visualizing Classifier Performance with Imbalanced Data”, in 8th IAPR TC3 Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR 2018), Siena, 2018. (413.21 KB)
L. Zhou, Piras, L., Riegler, M., Lux, M., Dang-Nguyen, D. - T., and Gurrin, C., “An Interactive Lifelog Retrieval System for Activities of Daily Living Understanding”, in Working Notes of {CLEF} 2018 - Conference and Labs of the Evaluation Forum, Avignon, France, September 10-14, 2018., 2018.
M. Jagielski, Oprea, A., Biggio, B., Liu, C., Nita-Rotaru, C., and Li, B., “Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning”, in 39th IEEE Symposium on Security and Privacy, 2018. (1.02 MB)
B. Ionescu, Müller, H., Villegas, M., de Herrera, A. García Se, Eickhoff, C., Andrearczyk, V., Cid, Y. Dicente, Liauchuk, V., Kovalev, V., Hasan, S. A., Ling, Y., Farri, O., Liu, J., Lungren, M., Dang-Nguyen, D. - T., Piras, L., Riegler, M., Zhou, L., Lux, M., and Gurrin, C., “Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation”, in Experimental {IR} Meets Multilinguality, Multimodality, and Interaction - 9th International Conference of the {CLEF} Association, {CLEF} 2018, Avignon, France, September 10-14, 2018, Proceedings, 2018, pp. 309–334.
D. - T. Dang-Nguyen, Piras, L., Riegler, M., Zhou, L., Lux, M., and Gurrin, C., “Overview of ImageCLEFlifelog 2018: Daily Living Understanding and Lifelog Moment Retrieval”, in Working Notes of {CLEF} 2018 - Conference and Labs of the Evaluation Forum, Avignon, France, September 10-14, 2018., 2018.
C. Gurrin, Schoeffmann, K., Joho, H., Dang-Nguyen, D. - T., Riegler, M., and Piras, L., “Proceedings of the 2018 {ACM} Workshop on The Lifelog Search Challenge, LSC@ICMR 2018, Yokohama, Japan, June 11, 2018”. {ACM}, 2018.
R. Soleymani, Granger, E., and Fumera, G., “Progressive Boosting for Class Imbalance and Its Application to Face Re-Identification”, Expert Systems With Applications, vol. 101, p. 21, 2018. (1.11 MB)
L. Putzu, Piras, L., and Giacinto, G., “Ten years of Relevance Score for Content Based Image Retrieval”, in 14th International Conference Machine Learning and Data Mining (MLDM), New York, 2018, vol. 10935.
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)
2017
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)
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)
I. Pillai, Fumera, G., and Roli, F., “Designing multi-label classifiers that maximize F measures: state of the art”, Pattern Recognition, vol. 61, 2017.

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