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Davide Ariu has graduated in July 2006 discussing a thesis titled "Detection of Attacks in Computer Networks with Hidden Markov Models". In January 2007 he has become a Ph.D. student of the Ph.D. Program in Electronic and Computer Engineering at the Department of Electrical and Electronic Engineering (University of Cagliari). From March to August 2007 he has been a visiting student at GTISC (Georgia Tech Information Security Center) working with the group leaded by prof. Wenke Lee.
In March 2010 he got a Ph.D. in Information Engineering at DRIEI defending a thesis titled "Host and Network based Anomaly Detectors for HTTP Attacks". From 2010 to 2012 he worked as a Post-doc in the PRA Group with a fellowship financed by the "Autonomous Region of Sardinia" (L.R. 7 Agosto 2009).
His research interests are focussed on the application of Pattern Recognition methods to Information Security Problems. In particular, he works on the analysis of the HTTP traffic aimed at the detection of attacks against web servers and at the analysis and detection of malwares. A list of publications is available here on this website or through the Google Scholar profile.
Recently, I served as reviewer for the following conferences and journals:
- CEAS, Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
- ICCCT, International Conference on Computer and Communications Technology
- MLDM, International Conference on Machine Learning and Data Mining
- Pattern Analysis and Applications, Springer.
- WSCG, International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision
- SCIA, Scandinavian Conference on Image Analysis
- ICIAP, International Conference on Image Analysis and Processing, Vietri sul Mare (Salerno) - Italy
- MLDM, International Conference on Machine Learning and Data Mining, Leipzig - Germany
- Computer Forensics
- Multiple Classifiers Systems
- Network Security: Analysis of the Network traffic aimed at the detection of attempts of intrusion
- Payload Analysis: detection of attacks against web servers based on the analysis of the HTTP Payload
- Malware Analysis/Detection: analysis and detection of HTTP-based Malware