Intelligent Systems Security: AI-Driven Approaches for Attacks, Detection & Explainability

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "ICT Infrastructures for Cybersecurity".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1231

Editor


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Guest Editor
Department of Computer Science, University of Almería, Ctra Sacramento, s/n, 04120 Almería, Spain
Interests: systems security; AI-enhanced security methodologies; traditional security techniques (signature recognition, rule-based detection); pentesting; intrusion detection system (IDS); zero-day threat detection; attack execution analysis; IDS training and datasets; explainable artificial intelligence (XAI) for security; transparent security models; automated decision-making in cybersecurity

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute to the Special Issue "Intelligent Systems Security: AI-Driven Approaches for Attacks, Detection & Explainability".

As cyber threats grow increasingly sophisticated and pervasive, the security of systems has become a critical concern demanding innovative and robust solutions. Traditional security methodologies, while foundational, are often insufficient to counter the evolving landscape of modern attacks, including zero-day threats and advanced persistent threats. Concurrently, the integration of Artificial Intelligence (AI) into security frameworks offers unprecedented opportunities for enhanced detection, real-time response, and automated decision-making. However, the opacity of many AI models presents new challenges in trust, accountability, and interpretability.

This Special Issue aims to explore the synergistic convergence of traditional systems security and AI-enhanced approaches, with a particular focus on the entire security lifecycle. We seek contributions that bridge the gap between established techniques—such as security audits (pentesting), attack detection, signature recognition , rule-based intrusion detection and reverse engineering—and cutting-edge AI methodologies for advanced threat detection. Special emphasis will be placed on the role of Explainable AI (XAI) in developing transparent, interpretable models that not only detect attacks but also provide clear rationale for security decisions, fostering trust in automated systems.

We welcome original research articles, comprehensive reviews, and case studies that advance the field of intelligent systems security through innovative methodologies and practical implementations.

Selected topics include (but are not limited to) the following:

  • Systems security and AI-enhanced methodologies;
  • Traditional security techniques (pentesting, signature recognition, rule-based detection, reverse engineering);
  • Security lifecycle management from detection to response;
  • Intrusion detection systems (IDSs);
  • IDS training and datasets;
  • Zero-day threat detection and analysis;
  • Attack execution analysis and threat modeling;
  • Explainable AI (XAI) for security interpretation;
  • Transparent security models and trustworthy AI;
  • Automated decision-making in cybersecurity;
  • AI-driven threat intelligence and incident response;
  • Adversarial machine learning and robust AI defenses;
  • Human–AI collaboration in security operations.

Prof. Dr. J. Gómez
Guest Editor

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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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computers is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • systems security
  • AI-enhanced security methodologies
  • traditional security techniques
  • pentesting
  • intrusion detection systems (IDSs)
  • zero-day threat detection
  • attack execution analysis
  • reverse engineering
  • explainable AI (XAI) for security
  • transparent security models
  • automated decision-making in cybersecurity
  • security lifecycle management
  • AI-driven threat detection
  • adversarial machine learning
  • human–AI collaboration in security
  • trustworthy AI Systems
  • real-time incident response

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Published Papers (1 paper)

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Research

18 pages, 533 KB  
Article
A Rigorous Comparative Study of Supervised Machine Learning Techniques for Network Anomaly Detection: Empirical Insights from the UNSW-NB15 Dataset
by Nouf Alkhater
Computers 2026, 15(5), 285; https://doi.org/10.3390/computers15050285 - 1 May 2026
Cited by 1 | Viewed by 892
Abstract
The increasing complexity of modern network infrastructures has intensified the need for reliable and efficient intrusion detection systems. While advanced deep learning approaches have demonstrated strong performance, their high computational cost and limited interpretability restrict their practical deployment in real-time environments. This study [...] Read more.
The increasing complexity of modern network infrastructures has intensified the need for reliable and efficient intrusion detection systems. While advanced deep learning approaches have demonstrated strong performance, their high computational cost and limited interpretability restrict their practical deployment in real-time environments. This study presents a systematic empirical evaluation of four supervised machine learning models—Decision Tree, Random Forest, Support Vector Machine (SVM), and XGBoost—for network anomaly detection using the UNSW-NB15 dataset. To ensure methodological rigor, a structured preprocessing pipeline and a five-fold stratified cross-validation framework were employed. Model performance was assessed using multiple evaluation metrics, including accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). In addition, a feature importance analysis was conducted to identify the most influential network traffic attributes contributing to anomaly detection. The results show that ensemble-based methods outperform individual classifiers, with XGBoost achieving the best overall performance (accuracy = 0.97, AUC = 0.98) along with high stability across validation folds. The analysis further reveals that a subset of flow-based and temporal features—such as sttl, sload, and dload—plays a critical role in distinguishing between normal and malicious traffic. This study provides a rigorous, interpretable, and reproducible benchmarking framework for supervised machine learning in network anomaly detection. The findings provide practical insights for developing efficient and scalable intrusion detection systems suitable for real-world deployment. Full article
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