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  <front>
    <journal-meta>
      <journal-id>authorea</journal-id>
      <publisher>
        <publisher-name>Authorea</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.36227/techrxiv.170326775.51644936/v1</article-id>
      <title-group>
        <article-title>Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author" corresp="yes">
          <contrib-id contrib-id-type="orcid">0000-0002-5714-8378</contrib-id>
          <name>
            <surname>Nowroozi</surname>
            <given-names>Ehsan</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name>
            <surname>Jadalla</surname>
            <given-names>Nada</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name>
            <surname>Ghelichkhani</surname>
            <given-names>Samaneh</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name>
            <surname>Jolfaei</surname>
            <given-names>Alireza</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date date-type="preprint" publication-format="electronic">
        <day>22</day>
        <month>12</month>
        <year>2023</year>
      </pub-date>
      <self-uri xlink:href="https://doi.org/10.36227/techrxiv.170326775.51644936/v1">This preprint is available at https://doi.org/10.36227/techrxiv.170326775.51644936/v1</self-uri>
      <kwd-group kwd-group-type="author-created">
        <kwd>Index Terms-Adversarial machine learning</kwd>
        <kwd>adversarial attacks</kwd>
        <kwd>adversarial learning</kwd>
        <kwd>adversarial machine learning</kwd>
        <kwd>anti counter forensics</kwd>
        <kwd>backdoor attack</kwd>
        <kwd>communication, networking and broadcast technologies</kwd>
        <kwd>corrupted training sets</kwd>
        <kwd>counter forensics</kwd>
        <kwd>cybersecurity</kwd>
        <kwd>label poisoning</kwd>
        <kwd>poisoning attack</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>
