Research Article
Characterizing Network Anomaly Traffic with Euclidean Distance-Based Multiscale Fuzzy Entropy
Table 2
Traffic volume of 13 types of Botnet scenarios.
| ID | Duration (hours) | Packets | Malware type | Infected hosts |
| 1 | 6.15 | 71971482 | Neris-1 | 1 | 2 | 4.21 | 71851300 | Neris-2 | 1 | 3 | 66.85 | 167730395 | Rbot-1 | 1 | 4 | 4.21 | 62089135 | Rbot-2 | 1 | 5 | 11.63 | 4481167 | Virut-1 | 1 | 6 | 2.18 | 38764357 | Menti | 1 | 7 | 0.38 | 7467139 | Sogou | 1 | 8 | 19.5 | 155207799 | Murlo | 1 | 9 | 5.18 | 115415321 | Neris-3 | 10 | 10 | 4.75 | 90389782 | Rbot-3 | 10 | 11 | 0.26 | 6337202 | Rbot-4 | 3 | 12 | 1.21 | 13212268 | NSIS.ay | 3 | 13 | 16.36 | 50888256 | Virut-2 | 1 |
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