AIC Seminar Series

We consider the problem of network anomaly detection in large distributed systems. In this setting, Principal Component Analysis (PCA) has been proposed as a method for discovering anomalies by continuously tracking the projection of the data onto a residual subspace. While successful empirically in moderate sized networks, this approach has serious scalability limitations. To overcome these limitations, we develop a PCA-based anomaly detector in which adaptive local data filters send to a coordinator just enough data to enable accurate global detection. Our method is based on a stochastic matrix perturbation analysis that characterizes the tradeoff between the accuracy of anomaly detection and the amount of data communicated over the network. This is joint work with Ling Huang, Minos Garofalakis, Michael I. Jordan, Anthony Joseph and Nina Taft.

XuanLong Nguyen is a PhD candidate in EECS, UC Berkeley. His research interests lie in machine learning, computational statistics, optimization and their applications to distributed/adaptive systems and sensor networks.

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