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Advances and Emerging Applications of Machine Learning, Evolutionary and Swarm...
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Advances and Emerging Applications of Machine Learning, Evolutionary and Swarm Intelligence in Cybersecurity

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 610

Special Issue Editors

Dr. Waleed Ali

E-Mail
Guest Editor
Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh 25729, Saudi Arabia
Interests: artificial intelligence; machine-learning techniques; feature selection; evolutionary and swarm optimization algorithms; fuzzy logic; neuro fuzzy systems; applications of machine learning; evolutionary and swarm optimization in healthcare and cybersecurity
Special Issues, Collections and Topics in MDPI journals
Dr. Talal Ali Ahmed Abdullah

E-Mail Website
Guest Editor
Interdisciplinary Research Center for Finance and Digital Economy, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Interests: explainable artificial intelligence (XAI); machine learning and deep learning; AI in healthcare; cybersecurity; signal processing

Special Issue Information

Dear Colleagues,

In recent years, the rapid evolution of digital systems has made cybersecurity one of the most data-intensive domains. As data volumes grow and applications become more interconnected, the need for intelligent algorithms that can learn, adapt and optimize has become essential to make smart decisions in various applications for cybersecurity. Currently, machine learning (ML), evolutionary computation (EC) and swarm intelligence (SI) have become fundamental components of modern intelligent systems in the cybersecurity domain since they can support efficient data processing, deep insight extraction and intelligent adaptation to evolving conditions.

This Special Issue aims to attract high-quality paper submissions to highlight recent advances and emerging applications of machine learning, evolutionary computation and swarm intelligence in addressing real-world challenges in cybersecurity.

The research domains include (but are not limited to) the following:

  • Malware detection
  • Phishing detection
  • Intrusion detection and anomaly detection
  • Fraud and financial crime analytics
  • Privacy-preserving and federated security learning
  • Adversarial ML and robust security models
  • Graph-based security analytics
  • IoT and edge security intelligence
  • Explainable AI for cybersecurity
  • EC and SI-driven cybersecurity optimization
  • Hyperparameter optimization and AutoML in cybersecurity
  • Feature selection and dimensionality reduction using EC and SI in cybersecurity

Dr. Waleed Ali
Dr. Talal Ali Ahmed Abdullah
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 250 words) can be sent to the Editorial Office for assessment.

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. Information is an international peer-reviewed open access monthly 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 1800 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

  • machine learning
  • evolutionary computation
  • swarm intelligence
  • cybersecurity analytics
  • malware detection
  • anomaly detection
  • phishing detection

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

Published Papers (1 paper)

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Research

28 pages, 2658 KB  
Article
Analysis of Robustness and Interpretability of Multinomial Naïve Bayes and Tiny Text CNN Models for SMS Spam Detection Under Adversarial Attacks
by Murad A. Rassam and Redhwan Shaddad
Information 2026, 17(5), 408; https://doi.org/10.3390/info17050408 - 24 Apr 2026
Viewed by 350
Abstract
The growing complexity of unwanted messages, especially SMS spam, presents a serious challenge to the security of digital communication and user experience. While conventional spam detection models are useful on clean datasets, they are vulnerable to targeted attacks that aim to evade detection. [...] Read more.
The growing complexity of unwanted messages, especially SMS spam, presents a serious challenge to the security of digital communication and user experience. While conventional spam detection models are useful on clean datasets, they are vulnerable to targeted attacks that aim to evade detection. This study is motivated by the urgent need to evaluate the resilience of machine learning models against evolving threats in real-world applications. We specifically investigate the robustness and interpretability of a Multinomial Naive Bayes (MNB) model, representative of traditional machine learning, and a Tiny Text convolutional neural network (Tiny Text CNN), representative of deep learning models, for SMS spam detection. Using the UCI dataset under simulated adversarial text attacks, both models were tested against filler-word insertion and character-level perturbation attacks. Results show that while the Tiny Text CNN maintained higher overall robustness (accuracy: 0.9821 clean vs. 0.9758 under character attacks), both models experienced notable degradation in recall, with MNB being more susceptible to filler-word attacks. Interpretability analyses using LIME and gradient-based saliency maps indicated that adversarial perturbations alter feature importance, diminishing the influence of spam-indicative tokens. The findings underscore the trade-offs between model complexity and adversarial resilience, offering insights for developing more secure and interpretable spam detection systems. Full article
(This article belongs to the Special Issue Advances and Emerging Applications of Machine Learning, Evolutionary and Swarm Intelligence in Cybersecurity)
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