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MStream outperforms scikit-learn algorithms in anomaly detection (arxiv.org)
5 points by adamnemecek 31 days ago | hide | past | favorite | 4 comments



Hi, I am one of the authors of the work. MStream detects anomalies, intrusions, DoS and DDoS attacks in real time and constant memory. It is built on top of MIDAS (https://github.com/Stream-AD/MIDAS/) and works in a multi-aspect data setting i.e., entries having multiple dimensions such as event-log data, multi-attributed graphs etc. MStream is two orders of magnitude faster while achieving higher accuracy on several publicly available datasets.

Github Repository: https://github.com/Stream-AD/MStream


Awesome work. Would you say this is the state of the art for real-time anomaly detection ?


MStream and MIDAS are more accurate than previous baselines for unsupervised anomaly detection. However, there can be scenarios where some labels (ground truth information) are known. In such cases, a semi-supervised algorithm might work better. We are currently working towards building a semi-supervised approach for anomaly detection in real-time.

To the best of my knowledge, MStream and MIDAS are the fastest and detect anomalies in real-time.


Auspex Labs is using MIDAS as part of our scoring for risk detection in network flow analysis.

https://www.auspex-labs.com/




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