New Insights into Network Security in the AI and Quantum Era
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 10 February 2026 | Viewed by 50
Special Issue Editor
Special Issue Information
Dear Colleagues,
The convergence of artificial intelligence (AI) and quantum computing (QC) is reshaping the future of network security. AI technologies such as deep learning, reinforcement learning, and autonomous threat detection are now central to cybersecurity strategies, enabling real-time anomaly detection, predictive analytics, and adaptive defense mechanisms. Simultaneously, the advent of quantum computing presents both opportunities and profound threats, rendering traditional cryptographic systems vulnerable while opening new frontiers in quantum-safe encryption and quantum key distribution. This Special Issue aims to present cutting-edge research, novel methodologies, and practical applications at the intersection of AI, QC, and cybersecurity. Contributions on a broad range of topics are welcome, including but not limited to the following:
- AI-based intrusion detection and mitigation strategies;
- Quantum-resistant encryption and post-quantum cryptography;
- Secure communication frameworks for quantum-enabled networks;
- Risk assessment and threat modeling using machine learning;
- Hybrid AI–QC approaches for enhancing data security.
This Special Issue seeks to bring together interdisciplinary perspectives from academia and industry to address the emerging challenges and opportunities relating to securing networks in the AI and quantum era.
Researchers and practitioners are invited to submit original research articles, reviews, and case studies that contribute to advancing the state of the art in this critical field.
Dr. Hassan Soubra
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence in cybersecurity
- quantum network security
- post-quantum cryptography
- AI-based intrusion detection
- quantum-safe communication
- machine learning for threat modeling
- hybrid AI–quantum approaches
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