Answers.Body: <p>If you are OK to use python, thy novelty-detection with sklearn:</p>
  
  <p><a href="https://scikit-learn.org/stable/modules/outlier_detection.html" rel="nofollow noreferrer">https://scikit-learn.org/stable/modules/outlier_detection.html</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-18T10:20:14.817Z
Answers.Text: I tried this but the results are not that convincing
Answers.UserDisplayName: Abishek
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-14T09:56:03.567Z
Answers.LastActivityDate: 2019-03-14T09:56:03.567Z
Answers.UserDisplayName: Vivek
Answers.Body: <p><a href="https://numenta.org" rel="nofollow noreferrer">Hierarchical Temporal Memory</a> is a model <a href="https://numenta.com/assets/pdf/whitepapers/Numenta%20White%20Paper%20-%20Science%20of%20Anomaly%20Detection.pdf" rel="nofollow noreferrer">well suited for anomaly detection</a>. It is also pretty interesting and different from currently typical Deep Learning models. </p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-03-14T10:44:35.133Z
Answers.Text: 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).
Answers.UserDisplayName: nbro
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Answers.CreationDate: 2019-03-14T09:56:20.197Z
Answers.LastActivityDate: 2019-03-14T09:56:20.197Z
Answers.UserDisplayName: BlindKungFuMaster
Body: <p>I need to create model which will find suspicious entries or anomalies in a network, whose characteristics or features are the <code>asset_id</code>, <code>user_id</code>, IP accessed from and <code>time_stamp</code>.</p>
  
  <p>Which unsupervised anomaly detection algorithms or models should I use to solve this task?</p>
  
Tags: <unsupervised-learning><anomaly-detection>
Title: Which unsupervised anomaly detection algorithms are there?
UserDisplayName: Abishek
ThreadText: # Which unsupervised anomaly detection algorithms are there?
  
  (Asked by: username_0 on 2019-03-14)
  
  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?
  
  **Tags:** unsupervised-learning, anomaly-detection
  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2019-03-14. Score: 1 | Accepted answer
  
  If you are OK to use python, thy novelty-detection with sklearn:
  
  <https://scikit-learn.org/stable/modules/outlier_detection.html>
  
  > **Comments:**
  > **username_0**: I tried this but the results are not that convincing  
  
  ---
  
  ### Answer by username_2 on 2019-03-14. Score: 1
  
  [Hierarchical Temporal Memory](https://numenta.org) is a model [well suited for anomaly detection](https://numenta.com/assets/pdf/whitepapers/Numenta%20White%20Paper%20-%20Science%20of%20Anomaly%20Detection.pdf). It is also pretty interesting and different from currently typical Deep Learning models.
  
  > **Comments:**
  > **username_3**: 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).  
  
  ---
  
  