Publications

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Journal Article
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)
R. Perdisci, Ariu, D., Fogla, P., Giacinto, G., and Lee, W., McPAD: A Multiple Classifier System for Accurate Payload-based Anomaly Detection, Computer Networks, vol. 53, pp. 864-881, 2009. (882.94 KB)
G. Giacinto, Perdisci, R., Del Rio, M., and Roli, F., Intrusion detection in computer networks by a modular ensemble of one-class classifiers, Information Fusion, vol. 9, 2008.
R. Perdisci, Corona, I., and Giacinto, G., Early Detection of Malicious Flux Networks via Large-Scale Passive DNS Traffic Analysis, IEEE Transactions on Dependable and Secure Computing, vol. 9, pp. 714-726, 2012. (1.37 MB)
R. Perdisci, Giacinto, G., and Roli, F., Alarm clustering for intrusion detection systems in computer networks, Engineering Applications of Artificial Intelligence, vol. 19, pp. 429-438, 2006.
Conference Paper
D. Ariu, Giacinto, G., and Perdisci, R., Sensing attacks in Computers Network with Hidden Markov Models, in Machine Learning and Data Mining in Pattern Recognition, MLDM 2007, Leipzig, 2007, vol. 4571, pp. 449-463. (315.94 KB)
G. Giacinto, Perdisci, R., and Roli, F., Network Intrusion Detection by Combining One-class Classifiers, in 13th International Conference on Image Analysis and Processing (ICIAP 2005), Cagliari, Italy, 2005, vol. 3617, pp. 58-65.
D. Ariu, Corona, I., Giacinto, G., Perdisci, R., and Roli, F., Intrusion Detection Systems based on anomaly detection techniques, in Italian Workshop on Privacy and Security (PRISE), Rome, 2007.
R. Perdisci, Corona, I., Dagon, D., and Lee, W., Detecting Malicious Flux Service Networks through Passive Analysis of Recursive DNS Traces, in Annual Computer Security Applications Conference (ACSAC), Honolulu, Hawaii, USA, 2009. (206.97 KB)
G. Giacinto, Perdisci, R., and Roli, F., Alarm Clustering for Intrusion Detection Systems in Computer Networks, in Machine Learning and Data Mining in Pattern Recognition (MLDM 2005), Leipzig, Germany, 2005, vol. 3587, pp. 184-193.