Connections the quarterly journal

Search form

  • Like Connections page on Facebook(link is external)
  • Follow us on Twitter(link is external)
  • Find connections on Istagram(link is external)
  • Connect with us on LinkedIN(link is external)
  • Subscribe to Connections RSS
  • English
  • Русский
  • Journal
    • Previews
    • Archive
    • Top papers
    • Athena Award
  • Submissions
    • Guidelines
    • Copyright information
    • Conflict of interest
    • Ethics in publishing
    • Themes
    • Calls for articles
    • Submit a paper
  • About
    • Mission
    • Editorial policy
    • Disclaimer
    • Editorial Board
    • Advisers
    • Author Index
    • Indexing & Archiving
    • Contact Us
    • Privacy policy
Share/Save

Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities

Publication Type:

Book Chapter

Authors:

Mohamed Alloghani; Dhiya Al-Jumeily; Abir Hussain; Jamila Mustafina; Thar Baker; Ahmed J. Aljaaf

Source:

Nature-Inspired Computation in Data Mining and Machine Learning, Springer Natur (2020)
  • 3529 reads
  • Google Scholar(link is external)
  • RTF
  • EndNote XML

References

Topham, Luke, Kashif Kifayat, Younis A. Younis, Qi Shi, and Bob Askwith. "Cyber Security Teaching and Learning Laboratories: A Survey." Information & Security: An International Journal 35, no. 1 (2016): 51-80.

Active calls for articles

Hybrid Operations

Deadline:
15.09.2025

Check also our Google Scholar profile(link is external)

  • Downloads
  • How to cite
No files have yet been downloaded.
APA style: Alloghani, M., Al-Jumeily D., Hussain A., Mustafina J., Baker T., & Aljaaf A. J. (2020).  Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities. Nature-Inspired Computation in Data Mining and Machine Learning.
Chicago style: Alloghani, Mohamed, Dhiya Al-Jumeily, Abir Hussain, Jamila Mustafina, Thar Baker, and Ahmed J. Aljaaf. "Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities." In Nature-Inspired Computation in Data Mining and Machine Learning. Springer Natur, 2020.
IEEE style: Alloghani, M., D. Al-Jumeily, A. Hussain, J. Mustafina, T. Baker, and A. J. Aljaaf, "Implementation of Machine Learning and Data Mining to Improve Cybersecurity and Limit Vulnerabilities", Nature-Inspired Computation in Data Mining and Machine Learning: Springer Natur, 2020.
  • Trending
  • Latest(active tab)
  • Most cited
  • The Weaponization of Emerging Technologies and Their Impact on Global Risk: A Perspective from the PfPC Emerging Security Challenges Working Group (1,624)
  • Goodbye Globalization? Hello 'Fragmentegration'! - The World Economy and Strategic Competition (1,225)
  • Towards a New Role for the European Union in the South Caucasus? (1,206)
  • Hybrid Threats and Strategic Competition (1,203)
  • The PfP Consortium Regional Stability in South East Europe Working Group at 25: The Transformed Balkans and the Work Ahead (1,111)
view all
  • Communicating (In)Security in Ukraine
  • Hybrid Warfare in the Black Sea Region: Russian Information-Psychological Operations in Georgia
  • Ukraine's Component in the Platform of European Memory and Conscience
  • Kremlin's 'War on Terrorism' in the Northeastern Caucasus: How Chechnya Still 'Saves' Russia
  • A Theory of Change: 25 Years of the Partnership for Peace Consortium
view all
Introduction to Program-based Defense Resource Management (8)
The Art of Shaping Defense Policy: Scope, Components, Relationships (but no Algorithms) (6)
NATO and the South Caucasus: Armenia, Azerbaijan, and Georgia on Different Tracks (5)
Terror-Crime Nexus? Terrorism and Arms, Drug, and Human Trafficking in Georgia (4)
Terrorist Routes in Central Asia: Trafficking Drugs, Humans, and Weapons (4)
view all

© Partnership for Peace Consortium of Defense Academies and Security Studies Institutes(link is external), 2012-2025
This site was designed and is maintained by Procon Ltd.(link is external), Executive publisher of Connections: The Quarterly Journal