Е.В. Бурнаев "Примеры решения задач с разными условиями на функцию выигрыша"

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Е.В. Бурнаев "Примеры решения задач с разными условиями на функцию выигрыша"
Место показа и дата: МФТИ, школа анализа данных (ШАД), 12.05.2012

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Е.В. Бурнаев "Примеры решения задач с разными условиями на функцию выигрыша"

  1. 1. Fig. Network volume anomalies in large-scale IP networks. Eachmeasurement corresponds to the cumulative number of bytes between two consecutive SNMP readings.
  2. 2. Fig. Approximation of real OD flows (dashed lines) by the spline-based (SB) model (full lines) in 3 operational networks. XtSMLE(k) is the real volume of OD flow k. Xt(k) stands for the estimated OD flow k using the SB model. XtTGE(k) is the estimated OD flow k using the tomogravity estimation method.
  3. 3. Fig. (a) RRMSE(t) and (b) cumulative RRMSE (t) for 672 measurements in Abilene and GEANT networks
  4. 4. Fig. (a) RRMSE(t) and (b) cumulative RRMSE (t) for 672 measurements in Abilene and GEANT networks. Fig. RRMSD(t) for 1500 flows in a Tier-2 ISP network
  5. 5. Fig. QQ-plots for 2 residual processes from (a) Abilene and (b) GEANT Fig. Correct detection rate vs. false alarm rate for the OSBD method(solid line) and the PCA approach, considering a different number of k first principal components uk to model the normal subspace.
  6. 6. Fig. Typical realizations of anomaly detection/isolation functions for a Tier-2 network (a and b) and Abilene (c and d).
  7. 7. Fig. On-line volume anomaly detection and isolation in Abilene, usingthe SSB method. The time between consecutive measurements is 5 min

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