UC Irvine

The design choices of the networked systems for autonomous & connected vehicles (CAV) play a critical role in the security and reliability of their operations and performance. Mainly, the networked systems for CAV should be resistant to disruptions, whether they are intentional or unintentional. For instance, the security of a given restricted airspace might be governed by an authentication system for authorizing drone activity over such airspace. However, having an open authentication channel for drones would leave them vulnerable for stalking due to the broadcast nature of authentication beacons. Furthermore, such authentication system would be fruitless without the means of detecting unauthorized and disruptive drones in the first place. On the other hand, the latency-sensitive nature of CAV applications requires reliable packet delivery schemes for resisting any foreseen disruptions.

In this Dissertation, we discuss the security and reliability perspectives of CAV. In the first Chapter of the Dissertation, we start by first proposing a drone detection framework that can detect the presence of flying drones by exploiting their emitted wireless traffic. Our work shows that the proposed framework achieves a detection accuracy up to 99% for both profiled and unprofiled drones, even in the presence of network interference and benign drone-like wireless traffic. Next we propose a privacy-preserving authentication scheme for drones which is influenced by the Security Credential Management System (SCMS), the official authentication system for V2V and V2I adopted by the U.S. Department of Transportation (USDoT). Our authentication scheme is then re-designed to be fitted with Remote-ID, the official identification framework for drones adopted by the Federal Aviation Administration (FAA). Finally, we propose our localized-authentication scheme that can pinpoint the physical location of authenticated vehicles and therefore differentiate between authorized and unauthorized vehicles.

In the second Chapter of the Dissertation, we go through different tools and techniques for testing and implementing reliable networked systems tailored to latency-sensitive applications such as CAV applications. First we present our packet injection tool that has various features such as generating customized traffic profiles using real data instead of padded data, creating different packetization policies, and producing fine-grained datasets. Then we go through our computing slicer module that can slice the computing resources of a device among different incoming traffic types based on different criteria such as customized scheduling policies. Next, we propose a state-recovery protocol for recovering the state of a channel in the case of channel disruptions using an adaptive loopback channel. Finally, we discuss our work on physical network slicing for 5G systems which is implemented on Colosseum, world’s largest RF emulator.