Zero-trust IoT security using homomorphic encryption, blockchain, and machine learning

Abstract

The growing deployment of internet of things (IoT) devices across healthcare, manufacturing, and smart infrastructure has introduced serious security vulnerabilities that can no longer be ignored. Traditional perimeter-based security models have proven inadequate for decentralized IoT environments, especially given that approximately 67% of these devices lack sufficient protection. To address this critical gap, this paper presents a unified security framework that brings together zero trust architecture (ZTA), homomorphic encryption (HE), blockchain, and machine learning (ML) into a single adaptive architecture. At its core, the framework employs Cheon-Kim-Kim-Song (CKKS) based HE for privacy-preserving computation, hyperledger fabric for decentralized identity and key management, and long short-term memory (LSTM) networks for real-time anomaly detection—all operating directly on encrypted data. We validated our approach through extensive experiments on 150,000 samples across 50 IoT devices, and the results were impressive. The framework achieved 97.2% threat detection accuracy with an average response latency of just 120 ms, along with 97.2% resistance to penetration attacks. Notably, it also reduced energy consumption by 25% compared to conventional LSTM implementations. What makes these findings particularly significant is that our framework outperformed both ZTA-only and blockchain-only baselines—by 5.7% and 8.9%, respectively—while maintaining complete data privacy. Furthermore, scalability analyses confirmed linear scaling to over 1,000 devices across 20 edge nodes. Taken together, these results position this framework as a mature, production-ready solution for large-scale IoT security deployments.