Hi Marvin,
You can use parameters like precision, recall, F1 score to evaluate the Anomaly Detection model you have trained but the results won't be meaningful as you have trained the model with data having no anomalies, therefore it is not trained to detect the anomalies. If the evaluation dataset also does not contain any anomalies, then there is a high probability that the scores will be really good but that doesn't ensure that the model is good and would also be able to detect anomalies in a future dataset.
Try to reduce the imbalance in the data to ensure a good model is trained that can detect both anomalies and normal data with high accuracy.
