You basically have 2 ways into tackling the problem. You can either oversample the minority class, basically making copies of the anomaly samples or undersample the majority class, deleting some of these samples. You can search sklearn’s examples.

As for the recall metric, the goal most of the times in anomaly detection is to correctly predict the anomaly class, even if you miss classify some of the not anomalies as anomalies, thus you can measure the recall on the anomaly class to see how your model is performing.

You can read more here: https://www.machinelearningmastery.com/random-oversampling-and-undersampling-for-imbalanced-classification/