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Artificial Intelligence Methods and Techniques to Cybersecurity

Submission Deadline: 10 February 2026 (closed) View: 25514 Submit to Special Issue

Guest Editors

Prof. José Braga de Vasconcelos

Email: jose.vasconcelos@ulusofona.pt

Affiliation: Faculty of Natural Sciences, Engineering and Tecnology ,Lusófona University, 400098, Porto, Portugal

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Research Interests: Knowledge Management, Knowledge Engineering, Software Engineering, Data Science, Artificial Intelligence, Machine Learning

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Dr. Hugo Barbosa

Email: hugo.barbosa@ulusofona.pt

Affiliation: Faculty of Natural Sciences, Engineering and Tecnology ,Lusófona University, 400098, Porto, Portugal

Homepage:

Research Interests: Cybersecurity, Computer Networks, Serious Games, Virtual Reality, Simulation, Player Adaptability

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Summary

The rapid evolution of cyber threats, including sophisticated phishing attacks, ransomware, and advanced persistent threats, demands innovative defense mechanisms. Artificial Intelligence (AI) has emerged as a transformative technology in cybersecurity, offering automated threat detection, intelligent risk assessment, and real-time response mechanisms to mitigate cyber risks effectively.

This Special Issue aims to explore cutting-edge AI techniques and methodologies applied to digital privacy and security. It will bring together researchers, industry professionals, and policymakers to present the latest advancements in AI-driven cybersecurity solutions, from deep learning-based intrusion detection to AI-powered encryption and anomaly detection. The issue will also examine ethical considerations, privacy risks, and the balance between AI-driven security and user rights.

Suggested Themes:

AI-powered threat detection and prevention in cybersecurity

Machine learning for anomaly and intrusion detection

AI-driven privacy-preserving techniques

Deep learning applications in digital forensics and malware analysis

The role of AI in identity management and authentication security

Ethical AI in cybersecurity: bias, fairness, and privacy concerns

AI for cloud security, IoT security, and blockchain-based protection

Adversarial AI and its implications for digital security

Keywords

Artificial Intelligence, Cybersecurity, Machine Learning, Security, Threat Detection, IoT, Cloud Security, Privacy, Data Encryption

Published Papers

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Open Access

ARTICLE

Adversarial Attack Defense in Graph Neural Networks via Multiview Learning and Attention-Guided Topology Filtering
Cheng Yang, Xianghong Tang, Jianguang Lu, Chaobin Wang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076126
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract Graph neural networks (GNNs) have demonstrated impressive capabilities in processing graph-structured data, yet their vulnerability to adversarial perturbations poses serious challenges to real-world applications. Existing defense methods often fail to handle diverse types of attacks and adapt to dynamic adversarial strategies because they typically rely on static defense mechanisms or focus narrowly on a single robustness dimension. To address these limitations, we propose an adversarial attention-based robustness strategy (AARS), which is a unified framework designed to enhance the robustness of GNNs against structural and feature perturbations. AARS operates in two stages: the first stage employs More >

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ARTICLE

MSA-ViT: A Multi-Scale Vision Transformer for Robust Malware Image Classification
Bofan Yang, Bingbing Li, Chuanping Hu
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077697
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract The rapid evolution of malware obfuscation and packing techniques significantly undermines the effectiveness of traditional static detection approaches. Transforming malware binaries into grayscale or RGB images enables learning-based classification, yet existing CNN- and ViT-based models depend heavily on fixed-resolution inputs and exhibit poor robustness under cross-resolution distortions. This study proposes a lightweight and sample-adaptive Multi-Scale Vision Transformer (MSA-ViT) for efficient and robust malware image classification. MSA-ViT leverages a fixed set of input scales and integrates them using a Scale-Attention Fusion (SAF) module, where the largest-scale CLS token serves as the query to dynamically aggregate cross-scale More >

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REVIEW

A Review on Penetration Testing for Privacy of Deep Learning Models
Salma Akther, Wencheng Yang, Song Wang, Shicheng Wei, Ji Zhang, Xu Yang, Yanrong Lu, Yan Li
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076358
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract As deep learning (DL) models are increasingly deployed in sensitive domains (e.g., healthcare), concerns over privacy and security have intensified. Conventional penetration testing frameworks, such as OWASP and NIST, are effective for traditional networks and applications but lack the capabilities to address DL-specific threats, such as model inversion, membership inference, and adversarial attacks. This review provides a comprehensive analysis of penetration testing for the privacy of DL models, examining the shortfalls of existing frameworks, tools, and testing methodologies. Through systematic evaluation of existing literature and empirical analysis, we identify three major contributions: (i) a critical… More >

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ARTICLE

A Comparative Analysis of Machine Learning Algorithms for Spam and Phishing URL Classification
Tran Minh Bao, Kumar Shashvat, Nguyen Gia Nhu, Dac-Nhuong Le
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.075161
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract The sudden growth of harmful web pages, including spam and phishing URLs, poses a greater threat to global cybersecurity than ever before. These URLs are commonly utilised to trick people into divulging confidential details or to stealthily deploy malware. To address this issue, we aimed to assess the efficiency of popular machine learning and neural network models in identifying such harmful links. To serve our research needs, we employed two different datasets: the PhiUSIIL dataset, which is specifically designed to address phishing URL detection, and another dataset developed to uncover spam links by examining the… More >

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ARTICLE

Bridging AI and Cyber Defense: A Stacked Ensemble Deep Learning Model with Explainable Insights
Faisal Albalwy, Muhannad Almohaimeed
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.075098
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract Intrusion detection in Internet of Things (IoT) environments presents challenges due to heterogeneous devices, diverse attack vectors, and highly imbalanced datasets. Existing research on the ToN-IoT dataset has largely emphasized binary classification and single-model pipelines, which often show strong performance but limited generalizability, probabilistic reliability, and operational interpretability. This study proposes a stacked ensemble deep learning framework that integrates random forest, extreme gradient boosting, and a deep neural network as base learners, with CatBoost as the meta-learner. On the ToN-IoT Linux process dataset, the model achieved near-perfect discrimination (macro area under the curve = 0.998),… More >

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ARTICLE

AFI: Blackbox Backdoor Detection Method Based on Adaptive Feature Injection
Simin Tang, Zhiyong Zhang, Junyan Pan, Gaoyuan Quan, Weiguo Wang, Junchang Jing
CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073798
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract At inference time, deep neural networks are susceptible to backdoor attacks, which can produce attacker-controlled outputs when inputs contain carefully crafted triggers. Existing defense methods often focus on specific attack types or incur high costs, such as data cleaning or model fine-tuning. In contrast, we argue that it is possible to achieve effective and generalizable defense without removing triggers or incurring high model-cleaning costs. From the attacker’s perspective and based on characteristics of vulnerable neuron activation anomalies, we propose an Adaptive Feature Injection (AFI) method for black-box backdoor detection. AFI employs a pre-trained image encoder… More >

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ARTICLE

AI-Driven Identification of Attack Precursors: A Machine Learning Approach to Predictive Cybersecurity
Abdulwahid Al Abdulwahid
CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1751-1777, 2025, DOI:10.32604/cmc.2025.066892
（This article belongs to the Special Issue: Artificial Intelligence Methods and Techniques to Cybersecurity)
Abstract The increasing sophistication of cyberattacks, coupled with the limitations of rule-based detection systems, underscores the urgent need for proactive and intelligent cybersecurity solutions. Traditional intrusion detection systems often struggle with detecting early-stage threats, particularly in dynamic environments such as IoT, SDNs, and cloud infrastructures. These systems are hindered by high false positive rates, poor adaptability to evolving threats, and reliance on large labeled datasets. To address these challenges, this paper introduces CyberGuard-X, an AI-driven framework designed to identify attack precursors—subtle indicators of malicious intent—before full-scale intrusions occur. CyberGuard-X integrates anomaly detection, time-series analysis, and multi-stage… More >

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Artificial Intelligence Methods and Techniques to Cybersecurity

Guest Editors

Summary

Keywords

Published Papers

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77

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17

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2

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66

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17

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188

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30

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1

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645

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190

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470

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88

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1

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587

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173

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3166

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2297

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