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  <doi_batch_id>aspg-3-2758-1791417849</doi_batch_id>
  <timestamp>20261008000409</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
  </depositor>
  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>Fusion: Practice and Applications</full_title>
    <abbrev_title>FPA</abbrev_title>
    <issn media_type="print">2770-0070</issn>
    <issn media_type="electronic">2692-4048</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>16</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Optimizing Message Response Time in IoT Security Using DenseNet and Fusion Techniques for Enhanced Real-Time Threat Detection</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Hitesh Kumar</given_name>
      <surname>Sharma</surname>
      <affiliations>
       <institution>
        <institution_name>Research Scholar, Amity University, Gwalior, M.P, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Samta Jain</given_name>
      <surname>Goyal</surname>
      <affiliations>
       <institution>
        <institution_name>Associate Professor, Amity University, Gwalior, M.P, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Sumit</given_name>
      <surname>Kumar</surname>
      <affiliations>
       <institution>
        <institution_name>Assistant Professor, G.N.S University, Sasaram, Bihar, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Abhishek</given_name>
      <surname>Kumar</surname>
      <affiliations>
       <institution>
        <institution_name>Research Scholar, Amity University, Gwalior, M.P, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>As IoT devices increase, accuracy and data security become increasingly crucial. This research recommends a powerful threat detection system that accelerates message responses to improve IoT security. The recommended strategy finds dangers in using many data sources. Our deep learning system is DenseNet. It groups photographs nicely. We show how the approach works using real-world experiments. It has few false positives and negatives and is effective at recognizing items. Through ablation research, we examine how design and component selections impact technique performance. This clarifies the method's fundamentals. The research reveals that feature selection, fusion, and DenseNet design improve the technique. We discuss the need for fine-tuning hyperparameters to improve approaches and monitor more individuals. The strategy makes IoT communities safer and more robust by laying the groundwork for threat detection and response. This approach solves message transmission delay concerns, making the IoT safer. These discoveries may benefit hacking specialists. They improve and speed up IoT security.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>101</first_page>
     <last_page>117</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">2758</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/FPA.160108</doi>
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