A Rank-SVM Approach to Anomaly Detection

We propose a novel non-parametric adaptive anomaly detection algorithm for high dimensional data based on rank-SVM. Data points are first ranked based on score s derived from nearest neighbor graphs on n-point nominal data. We then train a rank-SVM using this ranked data. A test-point is declared as an anomaly at α-false alarm level if the predicted score is in the α-percentile. The resulting anomaly detector is shown to be asymptotically optimal and adaptive in that for any false alarm rate α, its decision region converges to the α-percentile level set of the unknown underlying density. In addition we illustrate through a number of synthetic and real-data experiments both the statistical performance and computational efficiency of our anomaly detector.