  <?xml version="1.0"?>
<journal>
 <journal_metadata>
  <full_title>Journal of Cybersecurity and Information Management</full_title>
  <abbrev_title>JCIM</abbrev_title>
  <issn media_type="print">2690-6775</issn>
  <issn media_type="electronic">2769-7851</issn>
  <doi_data>
   <doi>10.54216/JCIM</doi>
   <resource>https://www.americaspg.com/journals/show/2382</resource>
  </doi_data>
 </journal_metadata>
 <journal_issue>
  <publication_date media_type="print">
   <year>2019</year>
  </publication_date>
  <publication_date media_type="online">
   <year>2019</year>
  </publication_date>
 </journal_issue>
 <journal_article publication_type="full_text">
  <titles>
   <title>A Comprehensive Approach to Cyberattack Detection in Edge Computing Environments</title>
  </titles>
  <contributors>
   <organization sequence="first" contributor_role="author">Department of Statistics and Programming, Faculty of Economics, University of Tishreen, Latakia, P.O. Box 2230, Syria </organization>
   <person_name sequence="first" contributor_role="author">
    <given_name>Khder</given_name>
    <surname>Khder</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of Food and Biotechnology, South Ural State University, 454080 Chelyabinsk </organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Alhumaima Ali</given_name>
    <surname>..</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Electronic and Computer Center, University of Diyala, Baqubah MJJ2+R9G, Iraq </organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Hussein</given_name>
    <surname>..</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia </organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Ammar</given_name>
    <surname>..</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia </organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Artem</given_name>
    <surname>..</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of System Programming, South Ural State University, 454080 Chelyabinsk, Russia </organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Irina</given_name>
    <surname>Potoroko</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Electronic and Computer Center, University of Diyala, Baqubah MJJ2+R9G, </organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>Mostafa</given_name>
    <surname>..</surname>
   </person_name>
   <organization sequence="first" contributor_role="author">Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt</organization>
   <person_name sequence="additional" contributor_role="author">
    <given_name>El-Sayed M El</given_name>
    <surname>El-kenawy</surname>
   </person_name>
  </contributors>
  <jats:abstract xml:lang="en">
   <jats:p>This research is concerned with the critical domain of cybersecurity in edge computing environments, which aims to strengthen defenses against increasing cyber threats that target interconnected Internet of Things (IoT) devices. The widespread adoption of edge computing introduces vulnerabilities that necessitate a strong framework for detecting cyberattacks. This study utilizes Long Short-Term Memory (LSTM) networks to present a comprehensive approach based on stacked LSTM layers for detecting and mitigating cyber threats in the dynamic landscape of edge networks. Using the NSL-KDD dataset and rigorous experimentation, this model demonstrates its ability to detect subtle anomalies in network traffic, which can be used to accurately classify malicious activities while minimizing false alarms. The findings highlight the potential of LSTM-based approaches to enhance security at the edge, providing promising avenues for strengthening IoT ecosystems’ integrity and resilience against emerging cyber threats.</jats:p>
  </jats:abstract>
  <publication_date media_type="print">
   <year>2024</year>
  </publication_date>
  <publication_date media_type="online">
   <year>2024</year>
  </publication_date>
  <pages>
   <first_page>69</first_page>
   <last_page>75</last_page>
  </pages>
  <doi_data>
   <doi>10.54216/JCIM.130107</doi>
   <resource>https://www.americaspg.com/articleinfo/2/show/2382</resource>
  </doi_data>
 </journal_article>
</journal>
