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Experience in measuring backbone traffic variability: models, metrics, measurements and meaning.

, , , , , and . Internet Measurement Workshop, page 91-92. ACM, (2002)

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Clear as MUD: Generating, Validating and Applying IoT Behavioral Profiles., , , , and . IoT S&P@SIGCOMM, page 8-14. ACM, (2018)A BasisEvolution framework for network traffic anomaly detection., , , , and . Comput. Networks, (2018)BasisDetect: a model-based network event detection framework., , , , , and . Internet Measurement Conference, page 451-464. ACM, (2010)Improving Hidden Markov Model Inferences With Private Data From Multiple Observers., and . IEEE Signal Process. Lett., 19 (10): 696-699 (2012)Real-time estimation of the parameters of long-range dependence., , and . IEEE/ACM Trans. Netw., 8 (4): 467-478 (2000)Modeling BGP Table Fluctuations., , , and . ITC, volume 4516 of Lecture Notes in Computer Science, page 141-153. Springer, (2007)Super-blockers and the Effect of Network Structure on Information Cascades., , and . WWW (Companion Volume), page 1435-1441. ACM, (2018)Estimating the Parameters of the Waxman Random Graph., , and . WAW, volume 11631 of Lecture Notes in Computer Science, page 71-86. Springer, (2019)On the identifiability of multi-observer hidden Markov models., and . ICASSP, page 1873-1876. IEEE, (2012)A Comparison of Poisson and Uniform Sampling for Active Measurements.. IEEE J. Sel. Areas Commun., 24 (12): 2299-2312 (2006)