Mathematical and Artificial Intelligence Applications in Network Information Security and Network Traffic Processing
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E1: Mathematics and Computer Science".
Deadline for manuscript submissions: 10 July 2026 | Viewed by 21
Special Issue Editors
Interests: network abnormal traffic analysis; timing and queue channel
Special Issue Information
Dear Colleagues,
The exponential growth of digital networks, coupled with the evolving landscape of cyber threats, has created unprecedented challenges in network security and traffic management. Contemporary cybersecurity demands sophisticated approaches that leverage advanced mathematical frameworks and artificial intelligence methodologies to detect, analyze, and mitigate complex network vulnerabilities and optimize traffic processing mechanisms.
This Special Issue seeks to showcase pioneering research that integrates mathematical modeling, machine learning, deep learning, and AI-driven methodologies to address critical challenges in network information security and traffic analysis. We particularly welcome contributions that link theoretical foundations with practical implementations, offering novel solutions to real-world network security problems.
We invite high-quality original research articles, comprehensive review papers, and case studies that present innovative approaches, theoretical breakthroughs, and empirical validations in this rapidly evolving interdisciplinary field.
Scope and Topics:
This Special Issue focuses on cutting-edge theoretical developments and practical applications in network security and traffic processing. Topics of interest include, but are not limited to, the following topics.
- Advanced Mathematical Models for Cybersecurity:
- Cryptographic protocols and security proofs;
- Game-theoretic approaches to network defense;
- Stochastic models for threat assessment.
- Network Traffic Modeling and Analysis:
- Traffic prediction and optimization algorithms;
- Quality of Service (QoS) mathematical frameworks;
- Network performance modeling and analysis.
- AI and Machine Learning in Network Security:
- Deep learning architectures for intrusion detection;
- Reinforcement learning for adaptive security systems;
- Federated learning approaches for distributed security.
- Intelligent Network Anomaly Detection:
- Real-time anomaly detection algorithms;
- Behavioral analysis and pattern recognition;
- Zero-day attack detection methodologies.
- Emerging Security Challenges and AI Solutions:
- Adversarial machine learning in cybersecurity;
- Privacy-preserving security mechanisms;
- Quantum-resistant security protocols.
- IoT and Edge Computing Security:
- Lightweight security algorithms for resource-constrained devices;
- Secure communication protocols for IoT networks;
- Edge-based security analytics.
Prof. Dr. Guangjie Liu
Dr. Jiangtao Zhai
Guest Editors
Manuscript Submission Information
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Keywords
- mathematical modeling
- network security
- encrypted traffic analysis
- AI/ML applications
- anomaly detection
- cybersecurity analytics
- network traffic optimization
- IoT security
- deep learning
- machine learning
- cryptography
- network performance analysis
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