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  <doi_batch_id>aspg-3-3223-1791419510</doi_batch_id>
  <timestamp>20261008003150</timestamp>
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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>2025</year>
    </publication_date>
    <journal_volume>
     <volume>17</volume>
    </journal_volume>
    <issue>2</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Cloud Environments</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Muhammad Asif</given_name>
      <surname>Khan</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Mohd Faizal Ab</given_name>
      <surname>Razak</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Zafril Rizal Bin M</given_name>
      <surname>Azmi</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Ahmad</given_name>
      <surname>Firdaus</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Abdul Hafeez</given_name>
      <surname>Nuhu</surname>
      <affiliations>
       <institution>
        <institution_name>Faculty of Computing, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pekan, Pahang, Malaysia</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Syed Shuja</given_name>
      <surname>Hussain</surname>
      <affiliations>
       <institution>
        <institution_name>College of Computer Sciences and Information Technology, Majmaah University, Majmaah, Saudi Arabia</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Distributed Denial of Service (DDoS) attacks pose a significant threat to cloud computing environments, necessitating advanced detection methods. This review examines the application of Machine Learning (ML) and Deep Learning (DL) techniques for DDoS detection in cloud settings, focusing on research from 2019 to 2024. It evaluates the effectiveness of various ML and DL approaches, including traditional algorithms, ensemble methods, and advanced neural network architectures, while critically analyzing commonly used datasets for their relevance and limitations in cloud-specific scenarios. Despite improvements in detection accuracy and efficiency, challenges such as outdated datasets, scalability issues, and the need for real-time adaptive learning persist. Future research should focus on developing cloud-specific datasets, advanced feature engineering, explainable AI, and cross-layer detection approaches, with potential exploration of emerging technologies like quantum machine learning.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>79</first_page>
     <last_page>97</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">3223</item_number>
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     <doi>10.54216/FPA.170207</doi>
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