MIDAS uses unsupervised learning to detect anomalies in a streaming manner in real-time and has become a new baseline. 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.
Check out MIDAS at https://github.com/Stream-AD/MIDAS
Siddharth Bhatia is a PhD student at National University of Singapore. Siddharth’s research is supported by a Presidents Graduate Fellowship and he has been recognized as a Young Researcher in the ACM Heidelberg Laureate Forum. Siddharth has done breakthrough work in streaming anomaly detection. His research, MIDAS, finds anomalies or malicious entities in real-time. MIDAS can be used to detect intrusions, Denial of Service (DoS), Distributed Denial of Service (DDoS) attacks, financial fraud and fake ratings. MIDAS also provides theoretical guarantees on the false positives and is three orders of magnitude faster than existing state of the art solutions.
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