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

1. Fig. Network volume anomalies in large-scale IP networks. Each
measurement corresponds to the cumulative number of bytes between
two consecutive SNMP readings.

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. Fig. (a) RRMSE(t) and (b) cumulative RRMSE (t) for 672 measurements
in Abilene and GEANT networks

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. 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. Fig. Typical realizations of anomaly detection/isolation functions for a
Tier-2 network (a and b) and Abilene (c and d).

7. Fig. On-line volume anomaly detection and isolation in Abilene, using
the SSB method. The time between consecutive measurements is 5 min