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Journal Article
R. Tronci, Piras, L., and Giacinto, G., Performance Evaluation of Relevance Feedback for Image Retrieval by “Real-World” Multi-Tagged Image Datasets, International Journal of Multimedia Data Engineering and Management (IJMDEM), vol. 3, p. 16, 2012. (357.99 KB)
G. Fumera, Roli, F., and Giacinto, G., Reject Option with Multiple Thresholds, Pattern Recognition, vol. 33, pp. 2099-2101, 2000.
R. Perdisci, Ariu, D., and Giacinto, G., Scalable Fine-Grained Behavioral Clustering of HTTP-Based Malware, Computer Networks - Special Issue on Botnet Activity: Analysis, Detection and Shutdown, vol. 57, pp. 487-500, 2013. (797.8 KB)
G. Giacinto, Roli, F., and Fumera, G., Selection of image classifiers, Electronics Letters, vol. 36, pp. 420-422, 2000.
D. Maiorca, Ariu, D., Corona, I., Aresu, M., and Giacinto, G., Stealth Attacks: An Extended Insight into the Obfuscation Effects on Android Malware, Computers And Security (Elsevier), vol. 51 (June), pp. 16-31, 2015. (413.99 KB)
L. Didaci, Giacinto, G., Roli, F., and Marcialis, G. L., A study on the performances of Dynamic Classifier Selection based on Local Accuracy Estimation, Pattern Recognition, vol. 38, pp. 2188-2191, 2005.
L. Piras and Giacinto, G., Synthetic Pattern Generation for Imbalanced Learning in Image Retrieval, Pattern Recognition Letters, vol. 33, p. 7, 2012. (236.64 KB)
E. Costamagna, Fanni, A., and Giacinto, G., A Tabu Search algorithm for the optimisation of telecommunication networks, European Journal of Operational Research, vol. 106, pp. 357-372, 1998.
A. Fanni, Giacinto, G., Marchesi, M., and Serri, A., Tabu search coupled with deterministic strategies for the optimal design of MRI devices, Int. J. of Applied Electromagnetics and Mechanics, vol. 10, pp. 21-31, 1999.
D. Maiorca, Biggio, B., and Giacinto, G., Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks, ACM Computing Surveys, vol. 52, no. 4, 2019. (1.21 MB)
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, vol. 16, no. 4, pp. 711-724, 2019. (3.61 MB)
J. Hoffmann, Rytilahti, T., Maiorca, D., Winandy, M., Giacinto, G., and Holz, T., Evaluating Analysis Tools for Android Apps: Status Quo and Robustness Against Obfuscation, Horst Görtz Institut for IT Security (Ruhr-University Bochum), Technical Report TR-HGI-2016-003, 2016. (519.16 KB)