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In-network PCA and anomaly detection

Huang Ling, Nguyen XuanLong, Garofalakis Minos, Jordan Michael I., Joseph Anthony, Taft Nina

Πλήρης Εγγραφή

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Περίληψη

We consider the problem of network anomaly detection in large distributed systems. In thissetting, Principal Component Analysis (PCA) has been proposed as a method for discoveringanomalies by continuously tracking the projection of the data onto a residual subspace.This method was shown to work well empirically in highly aggregated networks, that is,those with a limited number of large nodes and at coarse time scales. This approach, however,has scalability limitations. To overcome these limitations, we develop a PCA-basedanomaly detector in which adaptive local data filters send to a coordinator just enough datato enable accurate global detection. Our method is based on a stochastic matrix perturbationanalysis that characterizes the tradeoff between the accuracy of anomaly detection andthe amount of data communicated over the network.

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