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  <doi_batch_id>aspg-25-3817-1792098444</doi_batch_id>
  <timestamp>20261015210724</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>Journal of Cognitive Human-Computer Interaction</full_title>
    <abbrev_title>JCHCI</abbrev_title>
    <issn media_type="print">2771-1471</issn>
    <issn media_type="electronic">2771-1463</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>9</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Review of Machine Learning Technique Based Prediction Model for Phishing Websites Detection</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>RishiKesh</given_name>
      <surname>Dube</surname>
      <affiliations>
       <institution>
        <institution_name>Research Scholar, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Twinkle</given_name>
      <surname>Sharma</surname>
      <affiliations>
       <institution>
        <institution_name>Assistant Professor, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Damodar</given_name>
      <surname>Tiwari</surname>
      <affiliations>
       <institution>
        <institution_name>Professor, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Kailash</given_name>
      <surname>Patidar</surname>
      <affiliations>
       <institution>
        <institution_name>Professor, Dept. of CSE, Bansal Institute of Science and Technology, Bhopal, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Phishing attacks have emerged as a significant cybersecurity challenge, targeting individuals and organizations by tricking users into revealing sensitive information through deceptive websites. Traditional phishing detection methods, such as blacklists and heuristic-based approaches, struggle to keep pace with the rapid evolution of phishing techniques. Machine learning-based predictive models offer a promising solution by analyzing website attributes, URL structures, and behavioral patterns to distinguish between legitimate and phishing websites. This paper provides a comprehensive review of various machine learning techniques, including decision trees, support vector machines (SVM), random forests, deep learning models, and ensemble methods, employed in phishing website detection. It explores feature selection strategies, dataset characteristics, performance evaluation metrics, and real-world implementation challenges. Furthermore, the study discusses recent advancements such as adversarial resilience, natural language processing (NLP) integration, and real-time phishing detection frameworks. The review highlights existing research gaps and future directions to enhance phishing detection accuracy, scalability, and adaptability in evolving cybersecurity landscapes.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>17</first_page>
     <last_page>20</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3817</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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    <doi_data>
     <doi>10.54216/JCHCI.090204</doi>
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