This is also a possibility! I understand that in this case you only want to use „normal data“ to train your anomaly detection model in an unsupervised way. This approach was described here:
For example a popular approach is that you can learn your normal behaviour as „normal cluster“ and if a certain data point is too far away from this cluster conclude it is an anomaly.
Autoencoders for example are a popular choice for anomaly detection or you have a sufficient amount of normal labels and the problem is suiting.
This thread might be worth a look, too: Anomaly Detection with Different Probability Distributions - #5 by Christian_Simonis
Please let me know if this helps, @Rafael_Pachon_Alvare.
Best regards
Christian