Midas: Microcluster-Based Detector of Anomalies in Edge Streams


  • Siddharth Bhatia National University of Singapore
  • Bryan Hooi National University of Singapore
  • Minji Yoon Carnegie Mellon University
  • Kijung Shin KAIST
  • Christos Faloutsos Carnegie Mellon University




Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose Midas, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. Midas has the following properties: (a) it detects microcluster anomalies while providing theoretical guarantees about its false positive probability; (b) it is online, thus processing each edge in constant time and constant memory, and also processes the data 108–505 times faster than state-of-the-art approaches; (c) it provides 46%-52% higher accuracy (in terms of AUC) than state-of-the-art approaches.




How to Cite

Bhatia, S., Hooi, B., Yoon, M., Shin, K., & Faloutsos, C. (2020). Midas: Microcluster-Based Detector of Anomalies in Edge Streams. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3242-3249. https://doi.org/10.1609/aaai.v34i04.5724



AAAI Technical Track: Machine Learning