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Principal component analysis (PCA) is not only a fundamental dimensionreduction method, but is also a widely used network anomaly detectiontechnique. Traditionally, PCA is performed in a centralized manner, which haspoor scalability for large distributed systems, on account of the large networkbandwidth cost required to gather the distributed state at a fusion center.Consequently, several recent works have proposed various distributed PCAalgorithms aiming to reduce the communication overhead incurred by PCA withoutlosing its inferential power. This paper evaluates the tradeoff betweencommunication cost and solution quality of two distributed PCA algorithms on areal domain name system (DNS) query dataset from a large network. We also applythe distributed PCA algorithm in the area of network anomaly detection anddemonstrate that the detection accuracy of both distributed PCA-based methodshas little degradation in quality, yet achieves significant savings incommunication bandwidth.

On the performance overhead tradeoff of distributed principal component analysis via data partitioning