Due to the growing threat of climate change, the world’s governments have been encouraging the adoption of Electric Vehicles (EVs). Thus, EV numbers have been growing exponentially and gaining a significant market share. As a result, EV Charging Stations (EVCSs) are being rapidly deployed to satisfy the rising charging demand. These EVCSs are connected to a complex and interconnected system of cyber and physical components. However, without proper security in place, the EV charging load can become a weapon yielded by adversaries to destabilize the power grid. To this end, this thesis examines the security of the EV ecosystem and the impact of EV-based attacks against the grid. The thesis starts by examining the different components and technologies of the EV ecosystem before examining their vulnerabilities. We then 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 feed-back control theory to induce large frequency instabilities on the grid. To address the serious consequences of such attacks, two district detection methods are devised. The first 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 EV ecosystem. The second method is a detection scheme from the perspective of the utility aimed at detecting all kinds of LA attacks against the grid. This detector utilizes the grid’s mathematical model to preprocess the collected data and feed it to a Feature Fusion Neural Network (FFNN) that achieves 99.9 % detection accuracy while remaining robust against data poisoning. Finally, we show case the potential strength EVs can introduce into the grid once secured, by developing a robust LA attack mitigation scheme. This mitigation scheme utilizes the EV loads/injections to mitigate the impact of the three types of LA attacks. Additionally, we mathematically model the possible real-life uncertainties that hinder the operation of this mitigation scheme to achieve a robust performance.