Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework

Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework

연구 분야: Strategies

학회: ZTA-NextGen '24: Proceedings of the SIGCOMM Workshop on Zero Trust Architecture for Next Generation Communications

This paper introduces a robust zero-trust architecture (ZTA) tailored for the decentralized system that empowers efficient remote work and collaboration within IoT networks. Using blockchain-based federated learning principles, our proposed framework includes a robust aggregation mechanism designed to counteract malicious updates from compromised clients, enhancing the security of the global learning process. Moreover, secure and reliable trust computation is essential for remote work and collaboration. The robust ZTA framework integrates anomaly detection and trust computation, ensuring secure and reliable device collaboration in a decentralized fashion. We introduce an adaptive algorithm that dynamically adjusts to varying user contexts, using unsupervised clustering to detect novel anomalies, like zero-day attacks. To ensure a reliable and scalable trust computation, we develop an algorithm that dynamically adapts to varying user contexts by employing incremental anomaly detection and clustering techniques to identify and share local and global anomalies between nodes.