Abstract
This research focuses on the development of a synthetic traffic data generator implementing two
different statistical models identified as long-range and shortrange dependence. Normal network
traffic shows the long-range dependence feature by its traffic burstiness on different time scales.
It is believed that anomalous traffic such as worm or denial-of-service can change the
distribution to short-range dependence. In order to model long-range dependence of normal
network traffic, Fractional Gaussian Noise (FGN) with the Hurst parameter greater than 0.5 has
been implemented, while anomalous traffic is represented by FGN with Hurst parameter less
than or equal to 0.5. In addition, anomalous traffic has also been modeled by an AutoRegressive model (AR). As a result, a synthetic network traffic data generator tool has been
developed where the network traffic data can be generated easily based on pre-determined
parameters.