@article{(Open Science Index):https://publications.waset.org/pdf/1610,
	  title     = {Statistical Models of Network Traffic},
	  author    = {Barath Kumar and  Oliver Niggemann and  Juergen Jasperneite},
	  country	= {},
	  institution	= {},
	  abstract     = {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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {4},
	  number    = {1},
	  year      = {2010},
	  pages     = {177 - 185},
	  ee        = {https://publications.waset.org/pdf/1610},
	  url   	= {https://publications.waset.org/vol/37},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 37, 2010},