Adaptive Federated Threat Detection

Abstract: Timely detection and response to cyber threats have become increasingly challenging due to the distributed and dynamic nature of modern digital infrastructures. Conventional centralized intrusion detection systems struggle with scalability, delayed response, and privacy concerns when handling large volumes of security data. This project proposes an Adaptive Federated Threat Detection framework that employs federated learning to collaboratively identify malicious activities across multiple decentralized nodes without sharing raw data. Each participating node trains a local threat detection model using its own security logs, while a central coordinator aggregates encrypted model updates to build a global intelligence model. The system continuously adapts to evolving attack patterns by refining detection strategies based on real-time feedback. Experimental evaluation demonstrates improved detection accuracy, reduced false alarm rates, and enhanced robustness against emerging threats compared to traditional centralized security approaches, while preserving data confidentiality.

Keywords: Federated Learning, Adaptive Threat Detection, Cybersecurity, Distributed Intrusion Detection, Privacy-Preserving Machine Learning, Intelligent Security Systems