Real-time network data analysis using time series models

With the expansion of computer networks, there is a strong need for monitoring their properties in order to diagnose any problems and manage them in the best possible way. This monitoring is particularly useful if performed in real-time, however, such an approach is rather difficult (if not impossible) to implement in networks with increased traffic, using a passive monitoring scheme. One way to overcome this problem is to selectively sample network data, which in turn opens new issues such as how frequently this sampling should be performed, so as to obtain useful and exploitable data. In this work it is shown that it is possible to accurately represent high-speed network traffic using suitable time series models and then determine the size of the sampling window, so as to detect packet loss. The resulting scheme is scalable, protocol-independent and able to raise alerts in real time.