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Artificial Intelligence Methods and Techniques to Cybersecurity

Submission Deadline: 10 February 2026 View: 24560 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


  • Open Access

    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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