Connected autonomous vehicles (AVs) may offer new mobility options to millions of people. Integration of connectivity features into modern vehicles is a main driving force behind the ever-expanding attack surface of connected AVs, rendering them vulnerable to hacking and data theft. Key vulnerabilities arise from the increased coupling of unsecured automotive control networks with multimedia networks and the integration of wireless interfaces such as Bluetooth and Wi-Fi networks. As such, developing robust and reliable solutions to identify, localize, and mitigate cybersecurity threats to connected AVs is of societal importance. Existing solutions, however, are limited in their ability and scope as they are unable to reliably link the received data to the transmitting devices. The goal of this project is to safeguard AVs against growing attack surfaces and vectors by developing a holistic solution called the Linking2Source framework through three seamlessly integrated layers of defense, with each layer aiming to mitigate a specific set of attacks. The project also has a significant educational component, consisting of a set of inquisitive hands-on activities involving vehicle data acquisition, decoding, and data analytics, network packet injection, and intrusion detection aimed at outreach and broadening participation in STEM disciplines, including automotive cybersecurity, cyber-physical system security, statistical data analysis and digital forensics.