AI-Augmented Cybersecurity: Predictive Threat Intelligence Using Federated Learning

Abstract : The rapid evolution of cyber threats necessitates advanced, proactive defense mechanisms that can anticipate and mitigate attacks before significant damage occurs. This paper proposes an AI-augmented cybersecurity framework leveraging federated learning to enable predictive threat intelligence across distributed networks. Unlike traditional centralized models, federated learning allows multiple organizations to collaboratively train a global model without sharing sensitive local data, preserving privacy and enhancing security. Our framework integrates diverse data sources including network traffic logs, endpoint security alerts, and user behavior analytics to build a robust, multi-dimensional threat detection model. By employing advanced machine learning techniques such as deep neural networks and anomaly detection within the federated setting, the system effectively identifies emerging threats and zero-day attacks with high accuracy and low false-positive rates. Experimental evaluations on benchmark cybersecurity datasets demonstrate the framework’s superior performance in early threat prediction compared to conventional centralized approaches. Furthermore, the decentralized nature of federated learning ensures resilience against data breaches and adversarial attacks targeting the model itself. The proposed AI-driven approach also supports continual learning, enabling adaptive defense strategies in response to evolving threat landscapes. This work highlights the potential of combining federated learning with AI for scalable, privacy-preserving cybersecurity solutions that empower organizations to collaboratively strengthen their threat intelligence capabilities without compromising data confidentiality. The study offers insights into implementation challenges and future directions for integrating federated AI into operational cybersecurity infrastructures.

Keyword Federated Learning, Predictive Threat Intelligence, AI-Augmented Cybersecurity, Anomaly Detection, Privacy-Preserving Machine Learning, Distributed Security, Zero-Day Attack Detection, Collaborative Defense, Network Security, Adversarial Resilience