The extensive use of distributed vehicle platoon controllers has resulted in
several benefits for transportation systems, such as increased traffic flow,
fuel efficiency, and decreased pollution. The rising reliance on interconnected
systems and communication networks, on the other hand, exposes these
controllers to potential cyber-attacks, which may compromise their safety and
functionality. This thesis aims to improve the security of distributed vehicle
platoon controllers by investigating attack scenarios and assessing their
influence on system performance. Various attack techniques, including
man-in-the-middle (MITM) and false data injection (FDI), are simulated using
Model Predictive Control (MPC) controller to identify vulnerabilities and
weaknesses of the platoon controller. Countermeasures are offered and tested,
that includes attack analysis and reinforced communication protocols using
Machine Learning techniques for detection. The findings emphasize the
significance of integrating security issues into their design and
implementation, which helps to construct safe and resilient distributed platoon
controllers.
Distributed vehicle platoon controllers have proven to be a valuable tool for transportation systems, providing benefits such as improved traffic flow, fuel efficiency, and reduced pollution. However, the increased reliance on interconnected systems and communication networks also poses a potential vulnerability to cyber-attacks.
This thesis focuses on enhancing the security of distributed vehicle platoon controllers by exploring various attack scenarios and assessing their impact on system performance. The use of Model Predictive Control (MPC) controller allows for the simulation of attack techniques such as man-in-the-middle (MITM) and false data injection (FDI). By identifying vulnerabilities and weaknesses in the platoon controller, countermeasures can be developed and tested.
One important aspect addressed in this thesis is the multi-disciplinary nature of the concepts involved. The integration of security issues into the design and implementation of distributed platoon controllers requires expertise in both transportation systems and cybersecurity. By combining knowledge from these fields, it becomes possible to construct safe and resilient platoon controllers.
In addition to traditional security measures, this thesis proposes the use of Machine Learning techniques for detecting attacks. By analyzing communication protocols and patterns, it is possible to identify suspicious behavior that may indicate a cyber-attack. This approach adds an extra layer of defense to the platoon controller system.
Moving forward, it is crucial for researchers and engineers to continue exploring ways to improve the security of distributed vehicle platoon controllers. As transportation systems become increasingly interconnected and reliant on technology, the potential for cyber-attacks will continue to grow. By integrating security considerations into the design and implementation process, transportation systems can better protect against these threats and ensure the continued benefits of distributed vehicle platoon controllers.