Modern networked systems generate massive volumes of heterogeneous and continuously evolving data, creating major challenges for data management, intelligent analysis, and cybersecurity. Traditional machine learning approaches often struggle to adapt to the scale, decentralization, and dynamic nature of contemporary network environments, motivating the need for adaptive, distributed, and knowledge-driven AI solutions. This workshop explores recent advances in AI-driven network intelligence and cybersecurity, with a particular focus on information and knowledge management techniques for distributed systems. Topics include Generative AI (GenAI) for synthetic network data generation and augmentation, Large Language Models (LLMs) and Natural Language Processing (NLP) for cybersecurity intelligence and threat analysis, continual and federated learning for privacy-preserving adaptive systems, adversarial learning and trustworthy AI, and agentic AI for autonomous and human-in-the-loop security workflows. The workshop also investigates retrieval-augmented generation (RAG), knowledge graphs, reasoning over structured and unstructured security data, intelligent orchestration, and scalable AI-enabled network analytics. By bringing together researchers and practitioners from AI, cybersecurity, networking, and knowledge management, the workshop aims to foster interdisciplinary discussion on resilient, intelligent, and trustworthy networked systems.


AI4DEMONS seeks original, completed, and unpublished work not currently under review by any other journal/magazine/conference. Topics of interest include, but are not limited to:

-GenAI for synthetic network data generation, traffic simulation, and security data augmentation
-Agentic AI and human-in-the-loop systems for intelligent network management, orchestration, and cyber defense
-Data management, integration, provenance, and governance for complex, distributed, and heterogeneous networked systems
-Learning-based traffic analysis, anomaly detection, and classification for dynamic and evolving network environments
-AI-assisted network and systems configuration, optimization, orchestration, and autonomous decision-making
-CL, FL, and decentralized learning for adaptive and privacy-preserving network intelligence
-Adversarial learning, robustness, and trustworthy AI for attack generation, threat detection, and defense hardening
-AI-enabled security monitoring, intrusion detection, and threat analytics for distributed infrastructures
-Automated vulnerability discovery, exploit-chain analysis, prioritization, and remediation workflows
-NLP and LLMs for cybersecurity intelligence, incident triage, threat analysis, and security knowledge extraction
-RAG, knowledge graphs, and information retrieval techniques for cybersecurity and network intelligence applications
-Agentic and autonomous AI systems for penetration testing, attack simulation, and adaptive defense strategies
-Knowledge extraction and reasoning over structured and unstructured security and network data
-Explainability, accountability, safety, and robustness of AI-driven cybersecurity and network management solutions