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Anomalies are interesting because they tell a different story from the norm. Anomaly detection is used in many applications including detecting fraudulent credit card transactions and attacks in computer networks. But we do not want anomaly detection algorithms to be “alarm factories”, because if too many anomalies are detected on a regular basis, they tend to be ignored by the decision makers. Also, many anomaly detection methods have parameters that can only be set by experts, making them difficult to be used by lay people. Therefore, it is important to have “parameter-free” anomaly detection methods that minimize false positives.
In this talk, we introduce lookout, an anomaly detection method that uses extreme value theory and topological data analysis. Lookout is essentially parameter-free and has low false positive rates. We also delve into the world of computer networks and show how lookout can be used to detect suspicious nodes in computer network traffic.