A signal analysis of network traffic anomalies
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Properties and prediction of flow statistics from sampled packet streams
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
New directions in traffic measurement and accounting: Focusing on the elephants, ignoring the mice
ACM Transactions on Computer Systems (TOCS)
Estimating flow distributions from sampled flow statistics
Proceedings of the 2003 conference on Applications, technologies, architectures, and protocols for computer communications
Sketch-based change detection: methods, evaluation, and applications
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
Traffic matrix estimation on a large IP backbone: a comparison on real data
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Characterization of network-wide anomalies in traffic flows
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Aberrant Behavior Detection in Time Series for Network Monitoring
LISA '00 Proceedings of the 14th USENIX conference on System administration
Mining anomalies using traffic feature distributions
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
Impact of packet sampling on anomaly detection metrics
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Is sampled data sufficient for anomaly detection?
Proceedings of the 6th ACM SIGCOMM conference on Internet measurement
Inferring Original Traffic Pattern from Sampled Flow Statistics
SAINT-W '07 Proceedings of the 2007 International Symposium on Applications and the Internet Workshops
Towards optimal sampling for flow size estimation
Proceedings of the 8th ACM SIGCOMM conference on Internet measurement
Monitoring the application-layer DDoS attacks for popular websites
IEEE/ACM Transactions on Networking (TON)
Proceedings of the ACM SIGCOMM 2010 conference
Detection accuracy of network anomalies using sampled flow statistics
International Journal of Network Management
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We consider the mean-variance relationship of the number of flows in traffic aggregation, where flows are divided into several groups randomly, based on a predefined flow aggregation index, such as source IP address. We first derive a quadratic relationship between the mean and the variance of the number of flows belonging to a randomly chosen traffic aggregation group. Note here that the result is applicable to sampled flows obtained through packet sampling. We then show that our analytically derived mean-variance relationship fits well those in actual packet trace data sets. Next, we present two applications of the mean-variance relationship to traffic management. One is an application to detecting network anomalies through monitoring a time series of traffic. Using the mean-variance relationship, we determine the traffic aggregation level in traffic monitoring so that it meets two predefined requirements on false positive and false negative ratios simultaneously. The other is an application to load balancing among network equipments that require per-flow management. We utilize the mean-variance relationship for estimating the processing capability required in each network equipment.