Detection of Leaps/sLumps in Traffic Volume of Internet Backbone
APNOMS '08 Proceedings of the 11th Asia-Pacific Symposium on Network Operations and Management: Challenges for Next Generation Network Operations and Service Management
Maximum likelihood estimation of the flow size distribution tail index from sampled packet data
Proceedings of the eleventh international joint conference on Measurement and modeling of computer systems
International Journal of Network Management
Detection accuracy of network anomalies using sampled flow statistics
International Journal of Network Management
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We investigate how to detect network anomalies using flow statistics obtained through packet sampling. First, we show that network anomalies generating a huge number of small flows, such as network scans or SYN flooding, become dificult to detect when we execute packet sampling. This is because such flows are more unlikely to be sampled than normal flows. As a solution to this problem, we then show that spatially partitioning the monitored traffic into groups and analyzing the traffic of individual groups can increase the detectability of such anomalies. We also show the effectiveness of the partitioning method using network measurement data.