Fast Real-Time Anomaly Detection in Dynamic Graphs

In plain words

This is a software tool for detecting anomalies in dynamic graphs as they change in real time. It identifies unusual patterns and behaviors in network data, making it suitable for researchers and developers working with graph-based systems that need to spot irregularities quickly. The tool operates on live data streams rather than static datasets, which distinguishes it from traditional anomaly detection approaches.

written from the facts on this page · September 2026

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  Midas, a Streaming Anomaly Detector2020 · github.com · ▲11

  Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies. - Stream-AD/MIDAS

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  Midas, Microcluster-Based Detector of Anomalies in Edge Streams2019 · github.com · ▲15

  Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies. - Stream-AD/MIDAS

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  Midas, a Streaming Anomaly Detector. Now Implemented in Go2020 · github.com · ▲88

  Go implementation of MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams - steve0hh/midas

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  Fast Anomaly Detection in Graphs [pdf]2020 · comp.nus.edu.sg · ▲215

  The NUS School of Computing is recognised among the top global universities for computer science; and exemplifies academic excellence and innovative research.

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  Higher Order Sketch-Based Graph Anomaly Detection2021 · github.com · ▲55

  Sketch-Based Anomaly Detection in Streaming Graphs - Stream-AD/AnoGraph

• ID
  Intrusion Detection in Real-time2020 · arxiv.org · ▲69

  Abstract page for arXiv paper 1911.04464: MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams

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