<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462021000200423</article-id>
<article-id pub-id-type="doi">10.13053/cys-25-2-3939</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Detection of Flooding Attack on OBS Network Using Ant Colony Optimization and Machine Learning]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Takieddine Seddik]]></surname>
<given-names><![CDATA[Mohamed]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Kadri]]></surname>
<given-names><![CDATA[Ouahab]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bouarouguene]]></surname>
<given-names><![CDATA[Chakir]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Brahimi]]></surname>
<given-names><![CDATA[Houssem]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Faculty of MI Department of Computer Science ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Algeria</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Laboratory of Automation and Manufacturing Engineering  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Algeria</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2021</year>
</pub-date>
<volume>25</volume>
<numero>2</numero>
<fpage>423</fpage>
<lpage>433</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462021000200423&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462021000200423&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462021000200423&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Optical burst switching (OBS) has become one of the best and widely used optical networking techniques. It offers more efficient bandwidth usage than optical packet switching (OPS) and optical circuit switching (OCS).However, it undergoes more attacks than other techniques and the Classical security approach cannot solve its security problem. Therefore, a new security approach based on machine learning and cloud computing is proposed in this article. We used the Google Colab platform to apply Support Vector Machine (SVM) and Extreme Learning Machine (ELM)to Burst Header Packet (BHP) flooding attack on Optical Burst Switching (OBS) Network Data Set.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Optical burst switching]]></kwd>
<kwd lng="en"><![CDATA[support vector machine]]></kwd>
<kwd lng="en"><![CDATA[extreme learning machine]]></kwd>
<kwd lng="en"><![CDATA[burst header packet]]></kwd>
<kwd lng="en"><![CDATA[cloud computing]]></kwd>
</kwd-group>
</article-meta>
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