Cyber Attack Prevention and Detection for Electric Vehicle Charging

Abstract

The increasing adoption of Electric Vehicles (EVs) is transforming the automotive landscape, driven by the need for a more sustainable transportation sector. To support the widespread use of EVs, an efficient and reliable charging infrastructure is essential. For this, several related communication protocols and backend systems have been established to manage power delivery, authorization, and billing. The main goals are the security of charge session payments and a power grid-friendly scheduling of EV charging loads. The charging of EVs, however, also involves various security risks in associated use-cases. On the one hand, regarding the use-case of charge authorization and billing, existing protocols fail to protect against relevant adversaries. As a result, backend operators are exposed to the risk of significant financial damages and EV users are exposed to severe privacy risks. On the other hand, regarding the use-case of charge session power control and load balancing, existing processes can be manipulated by compromised systems. As a result, adversaries may be able to cause severe physical damage to involved systems and potentially harm power grid operations. In this dissertation, we address selected security risks. For charge authorization and billing, we present three solutions to enhance the preventive security of EV charging protocols. More specifically, we present concepts (i) for the integration of crypto-agility and the use of Post-Quantum Cryptography (PQC), (ii) for the use of Self-Sovereign Identities (SSIs) to enhance EV user privacy, and (iii) for the adoption of a standardized authorization framework to reduce existing complexity. Regarding manipulations of charge session control, we present several concepts for the analysis, detection, and mitigation of related attacks. More specifically, we present (i) a feasibility analysis of resulting attacks on grid stability and a related co-simulation framework, (ii) different anomaly detection concepts for either large-scale coordinated attacks on the grid or attacks in individual charging sessions, (iii) approaches for improving detection performance, including a Generative Adversarial Network (GAN)-based Intrusion Detection System (IDS) optimization and a combination of large-scale and session-based detection, and (iv) methods for attack mitigation based on IDS outputs. All presented concepts are implemented and evaluated with regard to relevant criteria. Concepts for EV charging protocol security are evaluated regarding performance/usability criteria based on proof-of-concept implementations and regarding security/privacy criteria based on formal protocol analyses using the Tamarin prover. Concepts for the analysis, detection, and mitigation of session control-related attacks are implemented with simulation-based approaches to evaluate their effect on involved systems and their detection/mitigation performance. Used Tamarin models and simulation data are published for reproducibility and future use in related studies. Overall, our results show the presented concepts can provide a significant benefit to the security of EV charging in the sector’s future