A Rank-SVM Approach to Anomaly Detection
This provides a theoretically sound and computationally efficient method for anomaly detection in high-dimensional datasets, though it appears incremental as it builds on existing rank-SVM and nearest neighbor techniques.
The authors tackled anomaly detection in high-dimensional data by proposing a rank-SVM algorithm that uses nearest neighbor graphs to rank data points, achieving asymptotic optimality where the decision region converges to the alpha-percentile level set of the unknown density for any false alarm rate.
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 scores 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 alpha-false alarm level if the predicted score is in the alpha-percentile. The resulting anomaly detector is shown to be asymptotically optimal and adaptive in that for any false alarm rate alpha, its decision region converges to the alpha-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.