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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?

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

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

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  • $\begingroup$ Yes i am ok with python. will try this $\endgroup$ – Abishek Mar 14 at 9:59
  • $\begingroup$ I tried this but the results are not that convincing $\endgroup$ – Abishek Mar 18 at 10:20
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Hierarchical Temporal Memory is a model well suited for anomaly detection. It is also pretty interesting and different from currently typical Deep Learning models.

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  • $\begingroup$ 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). $\endgroup$ – nbro Mar 14 at 10:44

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