# Which unsupervised anomaly detection algorithms are there?

I need to create model which will find suspicious entries or anomalies in a network, whose characteristics or features are the asset_id, user_id, IP accessed from and time_stamp.

Which unsupervised anomaly detection algorithms or models should I use to solve this task?

## 2 Answers

If you are OK to use python, thy novelty-detection with sklearn:

https://scikit-learn.org/stable/modules/outlier_detection.html

• Yes i am ok with python. will try this – Abishek Mar 14 at 9:59
• I tried this but the results are not that convincing – Abishek Mar 18 at 10:20

Hierarchical Temporal Memory is a model well suited for anomaly detection. It is also pretty interesting and different from currently typical Deep Learning models.

• I would add the detail that HTM-based algorithms are better suited for cases where you expect a stream of data (i.e. you want to continuously learn and detect anomalies online). – nbro Mar 14 at 10:44