Implementation of a Scale Semi-Autonomous Platoon to Test Control Theory Attacks

With all the advancements in autonomous and connected cars, there is a developing body of research around the security and robustness of driving automation systems. Attacks and mitigations for said attacks have been explored, but almost always solely in software simulations. For this thesis, I led a team to build the foundation for an open source platoon of scale semi-autonomous vehicles. This work will enable future research into implementing theoretical attacks and mitigations. Our 1/10 scale car leverages an Nvidia Jetson, embedded microcontroller, and sensors. The Jetson manages the computer vision, networking, control logic, and overall system control; the embedded microcontroller directly controls the car. A lidar module is responsible for recording distance to the preceding car, and an inertial measurement unit records the velocity of the car itself. I wrote the software for the networking, interprocess, and serial communications, as well as the control logic and system control

Protecting infrastructure networks from disinformation

Massive amount of information shared on online platforms makes the verification of contents time-consuming. Concern arises when the misleading or false information, called "disinformation", is exposed to many online platform users who have potential to react on it. The spread of disinformation can cause malicious consequences such as damage to critical infrastructure networks such as electric power, gas, and water distribution networks. Imagine a fake electricity discount, shared by disinformation campaigns, is exposed to many users on Twitter encouraging them to shift their electricity usage to a specific peak hour. If the population of users who engage with the fake discount exceeds a threshold, a blackout can happen due to the overconsumption of electricity. Thus, users access and exposure to accurate information on time can reinforce the infrastructures which are backbone of well-being for societies and economic growth. In this dissertation, we propose solutions to protect infrastructure networks from disinformation campaigns. The solutions include: (i) an integrated epidemiological-optimization (EPO) model involving a mixed integer linear programming model (MIP) and SIR (Susceptible, Infected, Recovered) model to protect physical infrastructure networks by counter disinformation (accurate information) spread in information networks, (ii) a disinformation interdiction model to influence physical infrastructure commodity consumers with accurate information given the topology of social network, (ii) a robust mixed integer linear programming model to propose solutions superior to the original EPO model under uncertain spread of disinformation scenarios. We illustrate our proposed models with two different case studies: (i) a sub-network of the western US interconnection power grid located in Los Angeles County in California, and (ii) the New York City subway system