  In this paper we offer a preliminary study of the application of Bayesian
coresets to network security data. Network intrusion detection is a field that
could take advantage of Bayesian machine learning in modelling uncertainty and
managing streaming data; however, the large size of the data sets often hinders
the use of Bayesian learning methods based on MCMC. Limiting the amount of
useful data is a central problem in a field like network traffic analysis,
where large amount of redundant data can be generated very quickly via packet
collection. Reducing the number of samples would not only make learning more
feasible, but would also contribute to reduce the need for memory and storage.
We explore here the use of Bayesian coresets, a technique that reduces the
amount of data samples while guaranteeing the learning of an accurate posterior
distribution using Bayesian learning. We analyze how Bayesian coresets affect
the accuracy of learned models, and how time-space requirements are traded-off,
both in a static scenario and in a streaming scenario.
