%0 Journal Article
	%A Barath Kumar and  Oliver Niggemann and  Juergen Jasperneite
	%D 2010
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 37, 2010
	%T Statistical Models of Network Traffic
	%U https://publications.waset.org/pdf/1610
	%V 37
	%X Model-based approaches have been applied successfully
to a wide range of tasks such as specification, simulation, testing, and
diagnosis. But one bottleneck often prevents the introduction of these
ideas: Manual modeling is a non-trivial, time-consuming task.
Automatically deriving models by observing and analyzing running
systems is one possible way to amend this bottleneck. To
derive a model automatically, some a-priori knowledge about the
model structure–i.e. about the system–must exist. Such a model
formalism would be used as follows: (i) By observing the network
traffic, a model of the long-term system behavior could be generated
automatically, (ii) Test vectors can be generated from the model,
(iii) While the system is running, the model could be used to diagnose
non-normal system behavior.
The main contribution of this paper is the introduction of a model
formalism called 'probabilistic regression automaton' suitable for the
tasks mentioned above.
	%P 177 - 185