Novel Approaches for Deep Learning in Cybersecurity
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 July 2026 | Viewed by 1589
Special Issue Editors
Interests: machine learning; deep learning; sociable robots; security; affective computing; digital health; data analysis; healthcare systems
Interests: data security and privacy; data analysis; privacy enhancing technologies; usable security and privacy; public safety and education; social media; healthcare systems
Special Issue Information
Dear Colleagues,
The increasing complexity of digital systems and the expanding attack surface of connected technologies demand innovative and intelligent approaches to cybersecurity. Deep learning, with its ability to model nonlinear and high-dimensional data patterns, has emerged as a powerful tool in identifying, mitigating, and predicting cyber threats across diverse application domains. This Special Issue aims to highlight recent advances and novel methodologies in applying deep learning to cybersecurity, focusing on solutions that improve the accuracy, scalability, and resilience of intelligent security systems. We are particularly interested in contributions that explore real-time threat detection, adversarial robustness, privacy-preserving AI, and secure architectures for emerging environments such as cloud, edge, and IoT ecosystems.
Topics of interest include but are not limited to:
- Deep learning models for intrusion detection and malware classification;
- Federated and privacy-preserving learning for cybersecurity;
- Adversarial machine learning and defense mechanisms;
- Secure biometric authentication and identity management;
- Multimodal threat intelligence systems;
- Explainable AI (XAI) in cybersecurity applications;
- Applications of GNNs, transformers, and generative models in cyber defense.
Dr. Mehdi Ghayoumi
Dr. Kambiz Ghazinour
Guest Editors
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Keywords
- deep learning for cybersecurity
- AI-based threat detection
- privacy-preserving machine learning
- federated learning in security
- intrusion detection systems (IDS)
- adversarial machine learning
- secure neural networks
- biometric authentication
- cyber-trust modeling
- multimodal security analytics
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