A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit <a rel="external noopener" href="https://arxiv.org/pdf/1212.3669v2.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
A metric for software vulnerabilities classification
[article]
<span title="2014-07-21">2014</span>
<i >
arXiv
</i>
<span class="release-stage" >pre-print</span>
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<span title="2012-12-15">2012</span>
<span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1212.3669v1">arXiv:1212.3669v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/epe64m4mdjfs5ilqhyvtnhqg4u">fatcat:epe64m4mdjfs5ilqhyvtnhqg4u</a> </span>
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Vulnerability discovery and exploits detection are two wide areas of study in software engineering. This preliminary work tries to combine existing methods with machine learning techniques to define a metric classification of vulnerable computer programs. First a feature set has been defined and later two models have been tested against real world vulnerabilities. A relation between the classifier choice and the features has also been outlined.
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<a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1212.3669v2">arXiv:1212.3669v2</a>
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