Tipo di tesi
Tesi di laurea magistrale
Titolo
On Exploiting a Digital Twin to Train and Test Attack Detection Models in Cyber-Physical Systems
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
COMPUTER ENGINEERING
Parole chiave
- autonomous driving
- cyber-physical systems
- cybersecurity
- digital twin
- neural networks
Data inizio appello
17/11/2023
Consultabilità
Non consultabile
Data di rilascio
17/11/2026
Riassunto (Italiano)
Cyber-Physical Systems (CPSs) are a large class of systems characterized by cooperating hardware and software components, connected with the external world.
Cybersecurity is a relevant activity in CPSs, since they are often safety-critical, and safety must be guaranteed also in case of cyber-attacks.
Modern autonomous vehicles are highly computerized CPSs, thus providing a wide range of access points for a potential attacker, who could gain full control over the vehicle and turn off all safety measures installed on it.
Our work provides a methodology designed to exploit a digital twin in co-simulation to gather data used for training and testing attack detection models based on machine learning algorithms. The developed approach has been applied to a case study having a digital twin composed of two vehicles, where the first one chases the second one. Results obtained are presented and analyzed, showing an accuracy of 94% in detecting attacks on the cyber-physical system, using a Multi-Layer Perceptron neural network.
