Federico Turrin

Ritratto Federico Turrin

Curriculum
Computer Science and Innovation for Societal Challenges, XXXV series
Grant sponsor

Fondazione CARIPARO and Yarix srl
Supervisor
s
Mauro Conti
Co-supervisor
s
Luciano Gamberini
Contact
federico.turrin@phd.unipd.it

Project description
Cyber-Physical Systems are characterized by the deep complex intertwining among the physical world (e.g., through sensors or actuators) with the virtual world of information processing. In this context, Cyber-Physical Systems are revolutionizing our world creating new services and applications in a variety of sectors such as smart grids, health systems, and intelligent transportation systems. One of the main applications of Cyber-Physical Systems are the Industrial Control Systems. Cyber-physical security of Industrial Control Systems represents an actual and worthwhile research topic. We can consider the Cyber-Physical Systems as the foundation of many applications in Industrial Control Systems that need actuators and sensors to perform monitoring. Furthermore, since specific Industrial Control Systems are categorized as critical infrastructures, damaging or destroying them can cause serious problems to the population and the environment. Some Industrial Control Systems examples include refineries, power plants, nuclear plants, and water distribution systems. However, the recent evolution of smart automation favored a wild interconnection of the industrial systems, opening dangerous surfaces of vulnerabilities. In this scenario, also referred as Industry 4.0, the cyber-physical threats generated by the insecure interactions between Information Technology (IT) and Operation Technology (OT) networks could lead to destructive consequences for environments and population safety. To deal with cyber-physical security in the era of Big Data, modern systems require the implementation of innovative Machine Learning solutions. The aim of my project is to create awareness on potentially disrupting threats in the current design of future Cyber-Physical Systems, and to develop innovative detection techniques by leveraging Machine Learning algorithms.