RSU-Based Intrusion Detection and Autonomous Intersection Response Systems

Vehicular safety and efficiency has been an ongoing research topic since the creation of the automobile. Despite this, deaths due to vehicular accidents are still extremely common, with driver issues and errors causing a vast majority of them. In order to combat the safety risks, Connected and Autonomous Vehicles (CAV) and other smart solutions have been heavily researched. CAVs provide the means to increase the safety of travel as well as its efficiency. However, before connected vehicles can be deployed and utilized, safe and secure communication and standards need to be created and evaluated to ensure that the introduction of a new safety threat does not overshadow the one that is already being faced. As such, it is integral for Intelligent Transportation Systems (ITS) to prevent, detect and respond to cyberattacks.

This research focuses on the detection and response of ITS components to cyberattacks. An Intrusion Detection System (IDS) located on Roadside Units (RSU) was developed to detect misbehavior nodes. This model maintains a 98%-100% accuracy while reducing system overhead by removing the need for edge or cloud computing. A resilient Intrusion Response System (IRS) for a autonomous intersection was developed to protect again sybil attacks. The IRS utilizes adaptive switching between several intersection types to reduce delay by up to 78% compared to intersections without these defenses.