Enhance the attack detection capabilities for electric vehicle charging.

What is it about?

Intrusion Detection Systems (IDSs) are often considered to be an important security mechanism for different use-cases. The Electric Vehicle (EV) charging use-case is one example, with various research articles proposing IDS solutions. One issue in this context, however, is the lack of representative datasets with a variety of realistic attack scenarios, which are vital for evaluating IDSs. Especially concerning the cyber-physical aspects of EV charging, representative datasets are missing and related work usually relies on normal charging data with generating random anomalies or manual attack insertions. This can result in unrealistic or biased attack data. In this paper, we address this issue by proposing a Generative Adversarial Network (GAN)-based IDS training method for EV charging. For this, a GAN is used against a pre-trained IDS to generate attack data that avoids detection. Afterwards, the IDS can be re-trained under consideration of the new attack data in order to eliminate persistent gaps or biases in detection. We implement and evaluate the GAN-based training system. Our evaluation shows the ability of the GAN to identify flaws in existing IDS s. Additionally, we show the effectiveness of re-training IDS s with the GAN output

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Why is it important?

Continuous training and upgrading the IDS with normal working data is mandatory to keep false positive rate low. As a result of incorporating new data, the methodology to create also relevant attack data is also crucial to evaluate the newly trained model before deployment.