Autoencoder anamoly detection
You need normal data to train on. If you have abnormal instances also, those should be excluded from the training set. Having access to labeled abnormal/normal data is very useful for the validation and testset.
Anything that differs from the normal data (as learned by the autoencoder) is considered an anomaly.

If you have a lot of labeled abnormal and normal data, then you can consider using binary classification instead.