Thank you for the explanation.

I'm looking forward to M-Stream for multi-dimensional data - but I have one question for that. Is there some preferred approach for selecting features in multi-dimensional anomaly detection?

Because I wonder if given enough dimensions, everything would be anomalous. Kind of like p-hacking works (at p=0.05 one of twenty hypotheses is falsely accepted just by sheer luck).