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Combining Filtering and Statistical Methods for Anomaly Detection.

, , and . Internet Measurement Conference, page 331-344. USENIX Association, (2005)

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Estimating dynamic traffic matrices by using viable routing changes., , , , and . IEEE/ACM Trans. Netw., 15 (3): 485-498 (2007)Inferring traffic shaping and policy parameters using end host measurements., , and . INFOCOM, page 151-155. IEEE, (2011)Optimal probing with statistical guarantees for network monitoring at scale., , , , , and . Comput. Commun., (2022)Optimal Probing with Statistical Guarantees for Network Monitoring at Scale., , , , , and . CoRR, (2021)Characterizing mobile user habits: The case for energy budgeting., , , , , , and . INFOCOM Workshops, page 306-311. IEEE, (2015)Fast Flow Classification over Internet., , , and . CNSR, page 235-242. IEEE Computer Society, (2004)Combining Filtering and Statistical Methods for Anomaly Detection., , and . Internet Measurement Conference, page 331-344. USENIX Association, (2005)Traffic matrix tracking using Kalman filters., , , and . SIGMETRICS Perform. Evaluation Rev., 33 (3): 24-31 (2005)WebClass: adding rigor to manual labeling of traffic anomalies., , and . Comput. Commun. Rev., 38 (1): 35-38 (2008)How to identify and estimate the largest traffic matrix elements in a dynamic environment., , , , and . SIGMETRICS, page 73-84. ACM, (2004)