Anomaly detection based on efficient Euclidean projection

Abstract

Machine-learning algorithms are widely applied in traffic classification and anomaly detection. Due to the tremendous traffic on the network, an extremely challenging question arises: how to efficiently and accurately detect the anomalous flow from the backbone network. One solution is proposed, online anomaly-detection scheme, which is based on the sparse feature selection method, Lasso. The sparse feature selection can be efficiently solved by reformulating the problem as an optimization problem with an i¾?1-ball constraint. At the evaluation stage, the authors preprocessed the raw data trace from the trans-Pacific backbone link between Japan and the United States and generated an evaluation data set. Their empirical study shows that the feature selection step can be solved quickly by applying the efficient Euclidean projection method; indeed, doing so resolves the feature selection step faster than using three classical i¾?1-min solvers. In terms of overall accuracy, true positive rate, false positive rate, precision, and F-measure, the proposed scheme improves the quality of detection. Copyright © 2015John Wiley & Sons, Ltd.