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Applied Sciences
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Data Mining and Machine Learning in Cybersecurity
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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: 30 June 2025 | Viewed by 6614

Special Issue Editors

Prof. Dr. Zhaoquan Gu

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
Dr. Xiaoyang Wang

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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29 pages, 362 KiB  
Article
Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
by Brandon Williams and Lijun Qian
Appl. Sci. 2025, 15(11), 5930; https://doi.org/10.3390/app15115930 - 24 May 2025
Viewed by 155
Abstract
In an increasingly interconnected world, securing large networks against cyber-threats has become paramount as cyberattacks become more rampant, difficult, and expensive to remedy. This research explores data-driven security by applying semi-supervised machine learning techniques for intrusion detection in large-scale network environments. Novel methods [...] Read more.
In an increasingly interconnected world, securing large networks against cyber-threats has become paramount as cyberattacks become more rampant, difficult, and expensive to remedy. This research explores data-driven security by applying semi-supervised machine learning techniques for intrusion detection in large-scale network environments. Novel methods (including decision tree with entropy-based uncertainty sampling, logistic regression with self-training, and co-training with random forest) are proposed to perform intrusion detection with limited labeled data. These methods leverage both available labeled data and abundant unlabeled data. Extensive experiments on the CIC-DDoS2019 dataset show promising results; both the decision tree with entropy-based uncertainty sampling and the co-training with random forest models achieve 99% accuracy. Furthermore, the UNSW-NB15 dataset is introduced to conduct a comparative analysis between base models (random forest, decision tree, and logistic regression) when using only labeled data and the proposed models when using partially labeled data. The proposed methods demonstrate superior results when using 1%, 10%, and 50% labeled data, highlighting their effectiveness and potential for improving intrusion detection systems in scenarios with limited labeled data. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Cybersecurity)
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18 pages, 4563 KiB  
Article
Kashif: A Chrome Extension for Classifying Arabic Content on Web Pages Using Machine Learning
by Malak Aljabri, Hanan S. Altamimi, Shahd A. Albelali, Maimunah Al-Harbi, Haya T. Alhuraib, Najd K. Alotaibi, Amal A. Alahmadi, Fahd Alhaidari and Rami Mustafa A. Mohammad
Appl. Sci. 2024, 14(20), 9222; https://doi.org/10.3390/app14209222 - 11 Oct 2024
Cited by 1 | Viewed by 1445
Abstract
Search engines are significant tools for finding and retrieving information. Every day, many new web pages in various languages are added. The threats of cyberattacks are expanding rapidly with this massive volume of data. The majority of studies on the detection of malicious [...] Read more.
Search engines are significant tools for finding and retrieving information. Every day, many new web pages in various languages are added. The threats of cyberattacks are expanding rapidly with this massive volume of data. The majority of studies on the detection of malicious websites focus on English-language websites. This necessitates more studies on malicious detection on Arabic-content websites. In this research, we aimed to investigate the security of Arabic-content websites by developing a detection tool that analyzes Arabic content based on artificial intelligence (AI) techniques. We contributed to the field of cybersecurity and AI by building a new dataset of 4048 Arabic-content websites. We created and conducted a comparative performance evaluation for four different machine-learning (ML) models using feature extraction and selection techniques: extreme gradient boosting, support vector machines, decision trees, and random forests. The best-performing model was then integrated into a Chrome plugin, created based on a random forest (RF) model, and utilized the features selected via the chi-square technique. This produced plugin tool attained an accuracy of 92.96% for classifying Arabic-content websites as phishing, suspicious, or benign. To our knowledge, this is the first tool designed specifically for Arabic-content websites. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Cybersecurity)
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Review

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37 pages, 4273 KiB  
Review
Systematic Review: Anti-Forensic Computer Techniques
by Rafael González Arias, Javier Bermejo Higuera, J. Javier Rainer Granados, Juan Ramón Bermejo Higuera and Juan Antonio Sicilia Montalvo
Appl. Sci. 2024, 14(12), 5302; https://doi.org/10.3390/app14125302 - 19 Jun 2024
Cited by 3 | Viewed by 3839
Abstract
The main purpose of anti-forensic computer techniques, in the broadest sense, is to hinder the investigation of a computer attack by eliminating traces and preventing the collection of data contained in a computer system. Nowadays, cyber-attacks are becoming more and more frequent and [...] Read more.
The main purpose of anti-forensic computer techniques, in the broadest sense, is to hinder the investigation of a computer attack by eliminating traces and preventing the collection of data contained in a computer system. Nowadays, cyber-attacks are becoming more and more frequent and sophisticated, so it is necessary to understand the techniques used by hackers to be able to carry out a correct forensic analysis leading to the identification of the perpetrators. Despite its importance, this is a poorly represented area in the scientific literature. The disparity of the existing works, together with the small number of articles, makes it challenging to find one’s way around the vast world of computer forensics. This article presents a comprehensive review of the existing scientific literature on anti-forensic techniques, mainly DFIR (digital forensics incident response), organizing the studies according to their subject matter and orientation. It also presents key ideas that contribute to the understanding of this field of forensic science and details the shortcomings identified after reviewing the state of the art. Full article
(This article belongs to the Special Issue Data Mining and Machine Learning in Cybersecurity)
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