Statistical analysis of network traffic for adaptive faults detection.

This paper addresses the problem of normal operation baselining for automatic detection of network anomalies. A model of network traffic is presented in which studied variables are viewed as sampled from a finite mixture model. Based on the stochastic approximation of the maximum likelihood function, we propose baselining network normal operation, using the asymptotic distribution of the difference between successive estimates of model parameters. The baseline random variable is shown to be stationary, with mean zero under normal operation. Anomalous events are shown to induce an abrupt jump in the mean. Detection is formulated as an online change point problem, where the task is to process the baseline random variable realizations, sequentially, and raise alarms as soon as anomalies occur. An analytical expression of false alarm rate allows us to choose the design threshold, automatically. Extensive experimental results on a real network showed that our monitoring agent is able to detect unusual changes in the characteristics of network traffic, adapt to diurnal traffic patterns, while maintaining a low alarm rate. Despite large fluctuations in network traffic, this work proves that tailoring traffic modeling to specific goals can be efficiently achieved.