This repository provides the artifacts for the paper "Detection of Anomalies in Electric Vehicle Charging Sessions" which is currently under review.
This repository provides the artifacts for the paper ["Detection of Anomalies in Electric Vehicle Charging Sessions"](https://doi.org/10.1145/3627106.3627127) which was accepted in the 39th Annual Computer Security Applications Conference (ACSAC).
In the paper, we propose an Intrusion Detection System (IDS) for the detection attacks in Electric Vehicle (EV) charging session data. Here, we provide the corresponding (synthetic) data sets and source code, which were used in the evaluation of the proposed solution.
In the paper, we propose an Intrusion Detection System (IDS) for the detection attacks in Electric Vehicle (EV) charging session data. Here, we provide the corresponding (synthetic) data sets and source code, which were used in the evaluation of the proposed solution.
Specifically, we provide the normal and attack data sets for the ACN-based data and the ElaadNL-based data as well as the Python source code for classification-, anomaly detection-, and ensemble-based IDS evaluations.
Specifically, we provide the normal and attack data sets for the ACN-based data and the ElaadNL-based data as well as the Python source code for classification-, anomaly detection-, and ensemble-based IDS evaluations.
