Herramienta de chequeo de registro xml obtenido desde SEDICI (UNLP)

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<dc:identifier>http://sedici.unlp.edu.ar/handle/10915/24247</dc:identifier>
<dc:identifier>isbn:0-387-34655-4</dc:identifier>
<dc:title>Web attack detection using ID3</dc:title>
<dc:creator>García, Víctor H.</dc:creator>
<dc:creator>Monroy, Raúl</dc:creator>
<dc:creator>Quintana, Maricela</dc:creator>
<dc:date>2006-08</dc:date>
<dc:date>2006-08</dc:date>
<dc:date>2012-11-15T16:55:14Z</dc:date>
<dc:language>en</dc:language>
<dc:subject>Ciencias Informáticas</dc:subject>
<dc:subject>Web-based services</dc:subject>
<dc:subject>Security and protection (e.g., firewalls)</dc:subject>
<dc:description>Decision tree learning algorithms have been successfully used in knowledge discovery. They use induction in order to provide an appropriate classification of objects in terms of their attributes, inferring decision tree rules. This paper reports on the use of ID3 to Web attack detection. Even though simple, ID3 is sufficient to put apart a number of Web attacks, including a large proportion of their variants.&#13;
It also surpasses existing methods: it portrays a higher true-positive detection rate and a lower false-positive one. The ID3 output classification rules that are easy to read and so computer officers are more likely to grasp the root of an attack, as well as extending the capabilities of the classifier.</dc:description>
<dc:description>Applications in Artificial Intelligence - Applications</dc:description>
<dc:description>Red de Universidades con Carreras en Informática (RedUNCI)</dc:description>
<dc:type>Objeto de conferencia</dc:type>
<dc:type>Objeto de conferencia</dc:type>
<dc:rights>http://creativecommons.org/licenses/by-nc-sa/2.5/ar/</dc:rights>
<dc:rights>Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)</dc:rights>
<dc:format>application/pdf</dc:format>
</oai_dc:dc>

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        "Quintana, Maricela",
        "Web attack detection using ID3"
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        "Security and protection (e.g., firewalls)"
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    "description": "Decision tree learning algorithms have been successfully used in knowledge discovery. They use induction in order to provide an appropriate classification of objects in terms of their attributes, inferring decision tree rules. This paper reports on the use of ID3 to Web attack detection. Even though simple, ID3 is sufficient to put apart a number of Web attacks, including a large proportion of their variants.\r\nIt also surpasses existing methods: it portrays a higher true-positive detection rate and a lower false-positive one. The ID3 output classification rules that are easy to read and so computer officers are more likely to grasp the root of an attack, as well as extending the capabilities of the classifier.",
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    "title": "Web attack detection using ID3",
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