Robust Traffic Anomaly Detection With Principal Component Pursuit

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Executive Summary

Principal Component Analysis (PCA) is a statistical technique that has been used for data analysis and dimensionality reduction. It was introduced as a network traffic anomaly detection technique firstly in. Since then, a lot of research attention has been received, which results in an extensive analysis and several extensions. In the sensitivity of PCA to its tuning parameters, such as the dimension of the low-rank subspace and the detection threshold, on traffic anomaly detection was indicated. However, no explanation on the underlying reasons of the problem was given in. In further investigation on the PCA sensitivity was conducted and it was found that the PCA sensitivity comes from the inability of PCA to detect temporal correlations.

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