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

TL;DR

This paper presents a robust zero-trust architecture leveraging blockchain-based federated learning and anomaly detection to enhance security and trust in decentralized IoT networks, especially for remote collaboration.

Contribution

It introduces a novel framework combining blockchain, federated learning, and adaptive anomaly detection for secure decentralized device collaboration.

Findings

Enhanced security against malicious updates in federated learning.

Effective anomaly detection for zero-day attacks.

Scalable trust computation in decentralized systems.

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.