Robust Zero Trust Architecture: Joint Blockchain based Federated learning and Anomaly Detection based Framework
arxiv(2024)
Abstract
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. Future directions include scalability improvements,
Dirichlet process for advanced anomaly detection, privacy-preserving
techniques, and the integration of post-quantum cryptographic methods to
safeguard against emerging quantum threats.
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