<?xml version="1.0" encoding="UTF-8"?>
<doi_batch version="5.3.1" xmlns="http://www.crossref.org/schema/5.3.1" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1" xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" xsi:schemaLocation="http://www.crossref.org/schema/5.3.1 http://www.crossref.org/schema/deposit/crossref5.3.1.xsd">
 <head>
  <doi_batch_id>aspg-3-1673-1791417702</doi_batch_id>
  <timestamp>20261008000142</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
  </depositor>
  <registrant>American Scientific Publishing Group</registrant>
 </head>
 <body>
  <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>2023</year>
    </publication_date>
    <journal_volume>
     <volume>11</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Recent Trends on Sophisticated types of Flooding Attacks and Detection Methods based on Multi Sensors Fusion Data for Cloud Computing Systems</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Nafea A. Majeed</given_name>
      <surname>Alhammadi</surname>
      <affiliations>
       <institution>
        <institution_name>Research Laboratory in Algebra, Numbers Theory and Intelligent Systems RLANTIS, University of Monastir, Monastir, Tunisia; Department of Computer Sciences, Shatt Al-Arab University College, B</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Mohamed</given_name>
      <surname>Mabrouk</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Sciences, Shatt Al-Arab University College, Basrah, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Mounir</given_name>
      <surname>Zrigui</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Sciences, Shatt Al-Arab University College, Basrah, Iraq</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Data storage, software services, infrastructure services, and platform services are only some of the benefits of today's widespread use of cloud computing. Since most cloud services run via the internet, they are vulnerable to a comprehensive range of attacks that might end it the disclosure of sensitive information. The distributed denial-of-service (DDoS) is amongst the attacks that pose an active threat to the cloud environment and disrupts the provided services for the legitimate participants. The main aim of this review paper is to present the recent trends on sophisticated flooding attacks detection methods for cloud computing systems. The review only considers the papers published within the period of 2014 until 2022.This study aims to examine the various deep learning-based DDoS detection algorithms and machine learning used across different cloud environments. Also, the study covers the Sophisticated types of Flooding Attacks and the testing dataset. The review outcomes several research challenges, gaps and future research guidelines related to protection of DDoS attack in cloud computing environment.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2023</year>
    </publication_date>
    <pages>
     <first_page>37</first_page>
     <last_page>56</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">1673</item_number>
    </publisher_item>
    <ai:program name="AccessIndicators">
     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
    </ai:program>
    <doi_data>
     <doi>10.54216/FPA.110103</doi>
     <resource>https://www.americaspg.com/journal/3/article/1673</resource>
     <collection property="text-mining">
      <item>
       <resource mime_type="application/pdf">https://www.americaspg.com/storage/81681000896.pdf</resource>
      </item>
     </collection>
    </doi_data>
   </journal_article>
  </journal>
 </body>
</doi_batch>
