PhD Oral Exam - Mohammad Ali Sayed, Information and Systems Engineering

When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.

Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.

Abstract

Due to the growing threat of climate change, the world’s governments have been encouraging the adoption of Electric Vehicles (EVs). As a result, EV numbers have been growing exponentially and have gained a significant market share among new vehicles. As a result, governments and operators have been rapidly deploying EV Charging Stations (EVCSs) to satisfy the charging rising demand. These EVCSs are connected to a complex and interconnected system of cyber and physical components. This EV ecosystem offers great possibilities in terms of reducing the emissions of the transportation sector and performing ancillary services to the power grid. However, without proper security measures in place, the EV charging load can become a weapon yielded by adversaries to destabilize the power grid. To this end, the work performed in this thesis examines the security posture of the EV ecosystem as well as the impact of EV-based attacks against the power grid. The thesis starts by examining the different components and technologies encompassed in the EV ecosystem before moving on to examine the cyber vulnerabilities in these components that leave them prone to attacks. After establishing the vulnerabilities that allow attackers to gain access to the charging process of the EVCSs, we demonstrate the greater impact that EV-based attacks can have on the grid as compared to traditional high-wattage smart loads attributed to their bi-directional power flow capabilities and their non-linear nature. We then propose a novel dynamic Load altering (LA) attack strategy that takes advantage of the special nature of the EV load as well as feed-back control theory to induce large frequency instabilities on the power grid. To address the serious consequences of such attacks, two district detection methods are devised. The first method is a two-tiered detector tailored specifically to be deployed on the EV ecosystem’s Central Management System (CMS) and EVCSs to detect attacks emanating from the ecosystem against the grid. The second method is a detection scheme from the perspective of the utility aimed at detecting all kinds of LA attacks against the grid, i.e., EV and otherwise. This detector takes advantage of the mathematical model representing the physical grid properties to preprocess the collected data and feed it to an advanced Feature Fusion Neural Network (FFNN) that achieves over 99.9 % detection accuracy while remaining robust against data poisoning attacks. Finally, we show case the potential strength EVs can introduce into the power grid once secured, by developing a robust LA attack mitigation scheme. This mitigation scheme utilizes the EV loads (and power injections) to mitigate the impact of the three known types of LA attacks even when the attack loads themselves are not known to the defender. Additionally, we mathematically model the possible real-life uncertainties that can hinder the operation of this mitigation scheme to achieve a robust performance under such conditions.