AIC Seminar Series
In-network PCA and anomaly detection
|XuanLong Nguyen||University of California, Berkeley|
Notice: hosted by Hung Bui
Date: 2006-12-01 at 15:00
Location: EJ228 (Directions)
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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