Data Mining and Machine Learning in Cybersecurity

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 190

Special Issue Editors


E-Mail Website
Guest Editor
School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518055, China
Interests: artificial intelligence security; cyber attack and defense; situation awareness analysis; big data analysis; intelligent connected vehicles; knowledge graph

E-Mail Website
Guest Editor
School of Computer Science and Engineering, University of New South Wales, Sydney 2052, NSW, Australia
Interests: graph processing; graph neural networks; spatial data processing

Special Issue Information

Dear Colleagues,

This Special Issue aims to showcase the latest advancements in the field of data mining and machine learning in cybersecurity. The information revolution has changed how we communicate all around the world and drawn unprecedented attention to network security issues. This Special Issue seeks to explore innovative techniques, methodologies, and tools that enhance our ability to detect, analyze, and respond to network security effectively.

Authors are invited to contribute original research papers and conceptual articles addressing various aspects of network attack detection and situation awareness analysis for comprehensive evaluation of various elements in the time and space environments of overall network security. This may include topics such as intrusion detection systems, anomaly detection algorithms, data mining/machine learning-driven approaches, threat intelligence integration, and real-time monitoring solutions.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of network security. Both theoretical and experimental studies are welcome, as are comprehensive review and survey papers.

Suitable topics include, but are not limited to, the following:

  • Network attack detection;
  • Situation awareness analysis;
  • Anomaly detection;
  • Intrusion detection systems;
  • Cyber threat analysis;
  • Network forensics;
  • In-vehicle network security;
  • Graph-based approaches for network security;
  • Cyber adversarial attacks and defenses;
  • Explainable artificial intelligence for network security.

Prof. Dr. Zhaoquan Gu
Dr. Xiaoyang Wang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cybersecurity
  • network attack
  • network defense
  • artificial intelligence
  • data mining

Published Papers

This special issue is now open for submission.
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