Advanced Threat Detection and Response Systems Using Federated Machine Learning in Critical Infrastructure
Keywords:
Federated Machine Learning, Threat Detection, Cybersecurity, Critical Infrastructure, Data Privacy, Anomaly Detection.Abstract
In the era of increasing cyber threats, critical infrastructure sectors such as energy, healthcare, and transportation face unprecedented challenges in ensuring robust cybersecurity. Traditional threat detection methods often fall short due to the dynamic nature of cyberattacks and the sheer volume of data generated across these systems. This paper introduces an innovative approach to threat detection and response by leveraging Federated Machine Learning (FML) to enhance cybersecurity across critical infrastructures. By allowing decentralized training of machine learning models while maintaining data privacy and security, FML enables organizations to collaborate in threat detection without sharing sensitive information. This study outlines the architecture of an advanced threat detection system utilizing FML, highlighting its efficiency in identifying anomalous patterns indicative of cyber threats. Through simulations and real-world case studies, the proposed system demonstrates superior detection accuracy and reduced false positive rates compared to conventional methods. Furthermore, the system's adaptive response mechanisms allow for real-time remediation actions, enhancing the resilience of critical infrastructure. The findings of this research underscore the potential of FML in transforming cybersecurity practices, offering a scalable and secure solution for protecting vital assets in an increasingly interconnected world.