Machine learning anomaly detection for continues data I need to find anomalies in logs created by network monitor tool Bro. Right now I am referring to the example given by Bro Analyze Tool. But the example only uses recent logs.

So I need to make it work with full log meaning to train on past logs and perform anomaly detection in real time on each new log. In the example, as far as I understand, unsupervised machine learning with IsolationForest and KMeans in scikit-learn python are used. 

So I have the following question.

For unsupervised machine learning is it possible to store the model with trained data which is already created on previous run?

If possible can I add new data with existing model and then perform anomaly detection in real time?
You can save sklearn models with pickle. This blog post shows how.