Wednesday 16 April 2025
As we increasingly rely on electric vehicles (EVs) to power our daily commutes, concerns about their charging infrastructure have come to the fore. A team of researchers has been working on a solution to detect anomalies in EV charging data, which could help prevent cyber attacks and ensure the reliable operation of these systems.
The study focused on detecting replay attacks, where hackers manipulate the communication between an electric vehicle and its charging station to gain unauthorized access or disrupt the charging process. These attacks can have serious consequences, including damage to the vehicle’s battery, financial losses for consumers, and even disruptions to the entire grid.
To tackle this problem, the researchers developed a novel approach using Temporal Convolutional Networks (TCNs) and Autoencoders (AEs). TCNs are designed to process sequential data, such as time-series data from EV charging stations. AEs, on the other hand, are neural networks that learn to compress and reconstruct data.
The team combined these two techniques to create a model that can identify anomalies in EV charging data. The approach involves training the TCN-AE model on a dataset of normal EV charging patterns, followed by testing it on data with simulated replay attacks. The model is then able to detect when an attack has occurred and alert the relevant authorities.
One of the key challenges in this research was dealing with the complexity of the data. EV charging stations generate vast amounts of data, including information about the vehicle’s state of charge, battery health, and charging speed. The researchers had to develop sophisticated algorithms to filter out irrelevant data and focus on the most important features that could indicate an anomaly.
The results are promising: the TCN-AE model was able to detect replay attacks with high accuracy, even when the attacks were subtle or occurred in isolation. This suggests that the approach has significant potential for real-world applications.
The study also highlights the importance of considering the cyber security risks associated with EV charging infrastructure. As more vehicles hit the roads and charging stations become increasingly connected, it is essential to develop robust systems that can detect and prevent attacks before they cause harm.
In the future, the researchers plan to extend their work by exploring other types of attacks and developing more sophisticated models that can adapt to changing patterns in EV charging data. They also hope to collaborate with industry partners to integrate their approach into real-world applications.
As we continue to rely on electric vehicles for our transportation needs, it is crucial that we prioritize the security and reliability of these systems.
Cite this article: “Detecting Deceptive Charging: A Novel Deep Learning Approach to Anomaly Detection in Electric Vehicle Charging Infrastructure”, The Science Archive, 2025.
Electric Vehicles, Charging Infrastructure, Cyber Attacks, Replay Attacks, Temporal Convolutional Networks, Autoencoders, Neural Networks, Anomaly Detection, Data Analysis, Security Risks
