Today’s large-scale electric vehicle (EV) infrastructures are heavily dependent on information communication technologies to maintain their operation and to support communication within sub-system components as well as the outside world. These technologies are vulnerable to various cyber and physical threats. Timely identification and mitigation of these threats are critical for improving human safety, avoiding economic losses, and preventing catastrophic system failures. By addressing this, our work presents a ResNet autoencoder (AE)-based cyber-physical anomaly detection system (Cy-Phy ADS) for detecting anomalies in EV controller area network (CAN) protocol communication. It consists of four main components: cyber-physical feature extractor, ResNet AE (RAE)-based anomaly detection (AD) framework, cyber-physical health metric (CPHM), and visualization dashboard. The presented framework was trained and tested using CAN data collected from the EV charging system testbed at the Idaho National Laboratory (INL). The presented Cy-Phy ADS was compared against six widely used unsupervised AD algorithms: one class support vector machine (OCSVM), variational AE (VAE), long short-term memory AE (LSTM AE), isolation forest (IForest), principal component analysis (PCA), and local outlier factor (LOF). The presented approach showed the highest accuracy among the compared methods. Further, the proposed approach showed comparable performance in terms of precision, F1, and false positive rate (FPR). It also showed the lowest training and inference time compared to the neural network (NN)-based baseline algorithms compared against with. Additionally, the Cy-Phy ADS has advantages such as unsupervised training, the ability to provide a holistic metric for system health characterization, and non-linear feature extraction.


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    Title :

    Cy-Phy ADS: Cyber-Physical Anomaly Detection Framework for EV Charging Systems




    Publication date :

    2024-12-01


    Size :

    2027670 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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