Leaky Batteries: A Novel Set of Side-Channel Attacks on Electric Vehicles

2025-03-11

AI Summary

This work identifies electric vehicle (EV) battery power consumption patterns as a novel side channel, enabling inference of driver identity, driving style, passenger count, and trip origin/destination solely from charge/discharge sequences—posing significant privacy and vehicular security risks. To address this, we propose and empirically validate the first battery-data-driven, multi-target side-channel attack framework, extending EV security analysis beyond conventional communication and control layers. Our approach integrates temporal feature engineering with deep and ensemble models—including LSTM, Random Forest, and XGBoost—to enable end-to-end analysis on both simulated and real-world EV battery datasets. Experimental results demonstrate an average attack success rate of 95.4% across all inference tasks, revealing that battery telemetry leaks substantially more sensitive information than previously assumed. This finding provides critical theoretical foundations and technical warnings for EV data governance and privacy-preserving design.

Technology Category

• Machine Learning: Privacy
• Application Domains: Mobility, Driving & Flight
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Advancements in battery technology have accelerated the adoption of Electric Vehicles (EVs) due to their environmental benefits. However, their growing sophistication introduces security and privacy challenges. Often seen as mere operational data, battery consumption patterns can unintentionally reveal critical information exploitable for malicious purposes. These risks go beyond privacy, impacting vehicle security and regulatory compliance. Despite these concerns, current research has largely overlooked the broader implications of battery consumption data exposure. As EVs integrate further into smart transportation networks, addressing these gaps is crucial to ensure their safety, reliability, and resilience. In this work, we introduce a novel class of side-channel attacks that exploit EV battery data to extract sensitive user information. Leveraging only battery consumption patterns, we demonstrate a methodology to accurately identify the EV driver and their driving style, determine the number of occupants, and infer the vehicle's start and end locations when user habits are known. We utilize several machine learning models and feature extraction techniques to analyze EV power consumption patterns, validating our approach on simulated and real-world datasets collected from actual drivers. Our attacks achieve an average success rate of 95.4% across all attack objectives. Our findings highlight the privacy risks associated with EV battery data, emphasizing the need for stronger protections to safeguard user privacy and vehicle security.

Problem

• Exploiting EV battery data for sensitive information extraction.
• Identifying drivers and habits using battery consumption patterns.
• Highlighting privacy risks in EV battery data exposure.

Innovation

• Machine learning models analyze EV power patterns.
• Feature extraction techniques identify sensitive user data.
• Side-channel attacks exploit battery consumption data.