Generating realistic workloads for network intrusion detection systems
WOSP '04 Proceedings of the 4th international workshop on Software and performance
Experimental validation of the ON-OFF packet-level model for IP traffic
Computer Communications
A practical method for weak stationarity test of network traffic with long-range dependence
MUSP'08 Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing
Nonstationarity of network traffic within multi-scale burstiness constraint
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
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The self-similarity of network traffic has been convincingly established based on detailed packet traces. This fundamental result promises the possibility of solving on-line and off-line traffic engineering problems using easily collectible coarse time-scale data, such as simple network management protocol measurements. This paper proposes a statistical model that supports predicting fine time-scale behavior of network traffic from coarse time-scale aggregate measurements. The model generalizes the commonly used fractional Gaussian noise process in two important ways: (1) it accommodates the recurring daily load patterns commonly observed on backbone links and (2) features of long range dependence and self-similarity are modeled only at fine time scales and are progressively damped as the time period increases. Using the data we collected on the Chinese Education and Research Network, we demonstrate that the proposed model fits 5-min data and generates 10-s aggregates that are similar to actual 10-s data.