Word count: 3000 words
Objectives to cover:
Introduction: Introduces the concept of federated threat intelligence enhanced by LLM-powered agents for secure cyber defense.
Background and Motivation: Explains the need for collaborative yet privacy-preserving cybersecurity solutions in distributed environments.
Federated Threat Intelligence Overview: Describes how federated systems enable decentralized threat data analysis without central data pooling.
Role of LLM Agents: Highlights the use of Large Language Models as intelligent agents for detecting, analyzing, and responding to cyber threats.
Privacy-Preserving Mechanisms: Outlines encryption, differential privacy, and secure aggregation techniques used to protect sensitive data.
System Architecture: Presents the overall design integrating federated learning, LLM agents, and secure communication layers.
Threat Detection and Response: Explains how LLM agents collaboratively identify, interpret, and mitigate cyber threats in real time.
Challenges and Future Directions: Discusses scalability, adversarial risks, and potential advancements in federated AI-driven defense systems.
Conclusion: Summarizes the study’s contributions toward achieving intelligent, collaborative, and privacy-preserving cyber defense.
Reference: IEEE Style