Deceiving Flexibility: A Stealthy False Data Injection Model in Vehicle-to-Grid Coordination

2026-03-18

AI Summary

This study addresses the risk of stealthy false data injection attacks in vehicle-to-grid (V2G) systems based on extended state-space models (eSSMs). The authors propose an attack strategy that manipulates only a subset of electric vehicles’ reported state-of-charge and power data, without compromising the control layer. By carefully crafting falsified data to align with the system’s aggregate model expectations, the attack remains undetected by conventional anomaly detection mechanisms. As the first work to demonstrate a purely data-layer stealth attack in V2G scenarios, the study shows through simulations that such manipulation can significantly degrade grid frequency stability without disrupting the physical charging or discharging processes. These findings expose a critical cybersecurity vulnerability in existing aggregative V2G architectures, highlighting the need for enhanced data integrity safeguards.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Multiagent Systems: Adversarial Agents
• Intelligent Robots: State Estimation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Electric vehicles (EVs) in Vehicle-to-Grid (V2G) systems act as distributed energy resources that support grid stability. Centralized coordination such as the extended State Space Model (eSSM) enhances scalability and estimation efficiency but may introduce new cyber-attack surfaces. This paper presents a stealthy False Data Injection Attack (FDIA) targeting eSSM-based V2G coordination. Unlike prior studies that assume attackers can disrupt physical charging or discharging processes, we consider an adversary who compromises only a subset of EVs, and limiting their influence to the manipulation of reported State of Charge (SoC) and power measurements. By doing so, the attacker can deceive the operator's perception of fleet flexibility while remaining consistent with model-based expectations, thus evading anomaly detection. Numerical simulations show that the proposed stealthy FDIA can deteriorate grid frequency stability even without direct access to control infrastructure. These findings highlight the need for enhanced detection and mitigation mechanisms tailored to aggregated V2G frameworks

Problem

• False Data Injection Attack
• Vehicle-to-Grid
• State of Charge
• Cybersecurity
• Grid Stability

Innovation

• False Data Injection Attack
• Vehicle-to-Grid
• State Space Model
• Stealthy Cyber Attack
• State of Charge