Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Analyzing and Storing Network Intrusion Detection Data using Bayesian
  Coresets: A Preliminary Study in Offline and Streaming Settings   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.

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The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat