Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized Insider Attack Detection Implementation using NetFlow Data   Insider Attack Detection in commercial networks is a critical problem that
does not have any good solutions at this current time. The problem is
challenging due to the lack of visibility into live networks and a lack of a
standard feature set to distinguish between different attacks. In this paper,
we study an approach centered on using network data to identify attacks. Our
work builds on unsupervised machine learning techniques such as One-Class SVM
and bi-clustering as weak indicators of insider network attacks. We combine
these techniques to limit the number of false positives to an acceptable level
required for real-world deployments by using One-Class SVM to check for
anomalies detected by the proposed Bi-clustering algorithm. We present a
prototype implementation in Python and associated results for two different
real-world representative data sets. We show that our approach is a promising
tool for insider attack detection in realistic settings.

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The categories of this paper are: atom-ph
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The categories of this paper are: chem-ph
2
The categories of this paper are: math
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
15
The categories of this paper are: nucl-ex
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The categories of this paper are: q-alg
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The categories of this paper are: alg-geom
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The categories of this paper are: econ
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The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: cmp-lg