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  <doi_batch_id>aspg-18-2755-1792028119</doi_batch_id>
  <timestamp>20261015013519</timestamp>
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   <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 Intelligent Systems and Internet of Things</full_title>
    <abbrev_title>JISIoT</abbrev_title>
    <issn media_type="print">2769-786X</issn>
    <issn media_type="electronic">2690-6791</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <journal_volume>
     <volume>12</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Strategizing IoT Network Layer Security Through Advanced Intrusion Detection Systems and AI-Driven Threat Analysis</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Deepak Dasaratha</given_name>
      <surname>Rao</surname>
      <affiliations>
       <institution>
        <institution_name>Indian Institute of Technology, Patna, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Akhilesh A.</given_name>
      <surname>Waoo</surname>
      <affiliations>
       <institution>
        <institution_name>Associate Dean and Head, CS/IT, AKS University, SATNA, MP, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Murlidhar Prasad</given_name>
      <surname>Singh</surname>
      <affiliations>
       <institution>
        <institution_name>Department of, C.S.&amp; E., B. P. Mandal College of Engineering, Madhepura, Bihar, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Piyush Kumar</given_name>
      <surname>Pareek</surname>
      <affiliations>
       <institution>
        <institution_name>Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru, Karnataka, India – 560064, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Shoaib</given_name>
      <surname>Kamal</surname>
      <affiliations>
       <institution>
        <institution_name>Department of ECE, Dr. B. R. Ambedkar Institute of Technology, Port Blair, Andaman &amp; Nicobar Islands, India-744103, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Shraddha V.</given_name>
      <surname>Pandit</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Artificial Intelligence and Data Science, PES Modern College of Engineering, Shivajinagar, Pune-411005, India</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>This research introduces an algorithmic framework for enhancing the security of Internet of Things (IoT) networks. The Enhanced Anomaly Detection (EAD) algorithm initiates the process by detecting anomalies in real-time IoT data, serving as the foundational layer. The Behavior Analysis for Profiling (BAP) algorithm builds upon EAD, adding behavior analysis for profiling and adaptive identification of abnormal behavior. Signature-Based Detection (SBD) involves pre-identified attack signatures, which supports detection of known attacks and provides proactive defense measures against documented threats. The MLID, or the Machine Learning-Based Intrusion Detection, algorithm uses trained machine learning models in order to detect anomalies and the adaptability to changing security risks. The Real-Time Threat Intelligence Integration (RTI) algorithm integrates updated threat intelligence feeds, which improves the framework's responsiveness to emerging threats. The visual representations illustrate once again the idea of the new framework being very accurate at intergration, applicability, and overal security effectiveness. The research makes a standard solution which proves to be a smart and responsive way guarding the IoT networks reducing and even fighting known and potential threats in a real-time mode.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2024</year>
    </publication_date>
    <pages>
     <first_page>195</first_page>
     <last_page>207</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">2755</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>
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     <doi>10.54216/JISIoT.120215</doi>
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