In-network PCA and anomaly detectionIn-network PCA and anomaly detection Πλήρης Δημοσίευση σε Συνέδριο Conference Full Paper 2015-12-012006enWe 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. 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-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.http://creativecommons.org/licenses/by/4.0/20th Annual Conference on Neural Information Processing Systems Huang Ling Nguyen XuanLong Garofalakis Minos Γαροφαλακης Μινως Jordan Michael I. Joseph Anthony Taft Nina Databases Management