Behavior-Aware Efficient Detection of Malicious EVs in V2G Systems

TL;DR

This paper introduces \\ouralg, a novel group testing scheme that combines machine learning predictions with robust algorithms to efficiently detect malicious electric vehicle drivers in vehicle-to-grid systems, validated on real-world data.

Contribution

It proposes a safety-enabled group testing algorithm that effectively integrates ML predictions with combinatorial methods for malicious EV detection in V2G systems.

Findings

extsc{ACN-Data} case studies validate \\ouralg's effectiveness.

The algorithm achieves near-optimal trade-offs between efficiency and robustness.

The approach advances algorithms with predictions in energy and transportation systems.

Abstract

With the rapid development of electric vehicles (EVs) and vehicle-to-grid (V2G) technology, detecting malicious EV drivers is becoming increasingly important for the reliability and efficiency of smart grids. To address this challenge, machine learning (ML) algorithms are employed to predict user behavior and identify patterns of non-cooperation. However, the ML predictions are often untrusted, which can significantly degrade the performance of existing algorithms. In this paper, we propose a safety-enabled group testing scheme, \ouralg, which combines the efficiency of probabilistic group testing with ML predictions and the robustness of combinatorial group testing. We prove that \ouralg is $O(d)$-consistent and $O(d\log n)$-robust, striking a near-optimal trade-off. Experiments on synthetic data and case studies based on \textsc{ACN-Data}, a real-world EV charging dataset validate the efficacy of \ouralg for efficiently detecting malicious users in V2G systems. Our findings contribute to the growing field of algorithms with predictions and provide insights for incorporating distributional ML advice into algorithmic decision-making in energy and transportation-related systems.