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T2 – Giorgio Giacinto

T2 – Giorgio Giacinto

Giorgio Giacinto is Full Professor of Computer Engineering at the University of Cagliari, Italy. Since 1995 he joined the research group on Pattern Recognition and Applications (PRA Lab), in which he leads the Computer Security unit. His research interests are in the field of pattern recognition and machine learning for cybersecurity, such as malware analysis and detection, web application security, malicious behaviour detection, phishing detection, etc. He is the coordinator of the MSc course in Computer Engineering, Cybersecurity and Artificial Intelligence at the University of Cagliari. He also serves as the International student mobility coordinator for the Faculty of Engineering and Architecture.

Prof. Giacinto has been serving either as the coordinator, or as a member of the technical management board, in many R&D projects at the local, national and European level. He is a member of the Cybersecurity National Lab of the CINI consortium where he serves as the contact point for the node at the University of Cagliari. He is also an active member of the WG6 (Scientific, Research and Innovation Agenda Working Group) of the European Cybersecurity Organization (ECSO), as a representative of the CINI consortium.

In 2015, he co-founded the spin-off company Pluribus One that is bringing to the market the most valuable products of the activities carried out by the PRA Lab research group.

Prof. Giacinto has been serving as a Program Committee member in international conferences and workshops, and he also serves as associate editor of the “Information Fusion” journal. He is author of more than 150 scientific papers in international journals and conferences. He is a senior member of both the IEEE and ACM.

 

June 4 – Machine Learnings for big data and cybersecurity

Security in the Cyberspace

The management of the security in the cyberspace requires the use of a variety of tools to early detect vulnerabilities, signs of misuse, and intrusions. Machine learning approaches are widely adopted for their capability of clustering and classifying events described by a large set of diverse characteristics. This lecture will start by exploring the meaning of the term security in the cyberspace, through a journey among the various components such as hardware, operating systems, networks, protocols and application software.  Then, different case studies will be presented to show the process that has been followed to use machine learning models for detecting malicious behaviours in the cyberspace.