Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AdversariaLib: An Open-source Library for the Security Evaluation of
  Machine Learning Algorithms Under Attack   We present AdversariaLib, an open-source python library for the security
evaluation of machine learning (ML) against carefully-targeted attacks. It
supports the implementation of several attacks proposed thus far in the
literature of adversarial learning, allows for the evaluation of a wide range
of ML algorithms, runs on multiple platforms, and has multi-processing enabled.
The library has a modular architecture that makes it easy to use and to extend
by implementing novel attacks and countermeasures. It relies on other
widely-used open-source ML libraries, including scikit-learn and FANN.
Classification algorithms are implemented and optimized in C/C++, allowing for
a fast evaluation of the simulated attacks. The package is distributed under
the GNU General Public License v3, and it is available for download at
http://sourceforge.net/projects/adversarialib.

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The categories of this paper are: q-alg
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: quant-ph
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The categories of this paper are: math-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
3
The categories of this paper are: physics
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The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph