Special Session 2

Special Session 2. AI-Driven Network Security and Intelligent Threat Detection

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• As communication networks evolve toward 6G, IoT, and cloud-native architectures, the attack surface expands dramatically, making traditional rule-based security mechanisms insufficient against sophisticated, adaptive threats. This special session focuses on cutting-edge research that leverages artificial intelligence to safeguard next-generation communication infrastructures. We invite original contributions on AI-driven intrusion detection, intelligent anomaly analysis, deep learning-based traffic classification, zero-day threat prediction, and autonomous defense mechanisms in mobile, wireless, and edge networks. Topics of interest also include trustworthy AI for security, adversarial robustness of detection models, federated learning for privacy-preserving threat intelligence, and real-time security orchestration in large-scale networks. By integrating advances from AI for communications and network security, this session aims to explore how intelligent threat detection can empower resilient, secure, and high-quality communication systems. We welcome theoretical breakthroughs, novel algorithms, system designs, and practical deployments that address the growing security challenges in modern communication networks.
  
  
• Topics
  T1: Deep learning-based network intrusion detection and attack classification in large-scale communication network
  T2: Multi-agent reinforcement learning for automated security testing and vulnerability assessment
  T3: Graph neural networks for attack graph analysis and threat propagation modeling
  T4: Real-time anomaly detection in encrypted network traffic using unsupervised learning
  T5: Large language models for automated vulnerability discovery and exploit generation
  T6: Intelligent security orchestration and automated response in software-defined networks
  T7: Adversarial machine learning attacks and defenses on network security systems
  T8: AI-powered malware detection and analysis in IoT and edge computing environments
  T9: Federated learning for collaborative threat intelligence sharing across networks
  T10: Explainable AI methods for interpretable network security decision-making