Synthetic Generation of Realistic Network Traffic
Abstract: The industry shows a clear need for synthetically generated realistic network traffic. As a possible solution, this thesis proposes a method for generating such data in an automatic and controllable manner. This thesis first examines the characteristics of real network traffic and analyzes the length of ON/OFF periods. The theory that network traffic exhibits self-similarity and high variability is once again tested and proven, thereby also the fact that the ON/OFF periods of real network traffic comes from a heavy-tailed distribution. Thereafter, the thesis proposes a way to simulate user interaction with real world applications by using a UI testing framework called WinAppDriver. This tool is then used to synthetically generate network traffic, of which the characteristics are analyzed and compared to that of real network traffic. The results show that the generated network traffic is indeed statistically similar to real network traffic. Finally, everything is combined by setting up a whole network of virtual machines with simulated users.
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