Post #233

Controlling Fake News using Graphs and Statistics This is a guest post by Siddharth Bhatia about their recent work with Christos Faloutsos on anomaly detection in streaming data. MIDAS, Microcluster-Based Detector of Anomalies in Edge Streams (AAAI 2020) uses unsupervised learning to detect anomalies in a streaming manner in real-time. It was designed keeping in mind the way recent sophisticated attacks occur. MIDAS can be used to detect intrusions, Denial of Service (DoS), Distributed Denial of Service (DDoS) attacks, financial fraud and fake ratings. MIDAS combines a chi-squared goodness-of-fit test with the Count-Min-Sketch (CMS) streaming data structures to get an anomaly score for each edge. It then incorporates temporal and spatial relations to achieve better performance. MIDAS provides theoretical guarantees on the false positives and is three orders of magnitude faster than existing state of the art solutions. Paper: https://arxiv.org/abs/1911.04464 Code: https://github.com/Stream-AD/MIDAS