Enhancing trust in machines integration with Dirichlet distribution and self-sovereign identity

In critical IoT ecosystems, where autonomous devices operate without human oversight, security hinges on robust identity management as the foundation of authentication. Traditional methods struggle to address dynamic threats and scalability demands, creating vulnerabilities in machine-to-machine trust. Self-sovereign identity (SSI) emerges as a transformative paradigm, enabling decentralized, cryptographically assured authentication while ensuring operational resilience. This paper proposes a dual-blockchain framework for IoT authentication, integrating cryptographic identity verification with behavioral trust analytics. The architecture consists of a decentralized identity blockchain for credential issuance and lifecycle management, alongside a transaction blockchain for validating device interactions, both coordinated through smart contracts across trust authorities, devices, and full nodes. The protocol combines static authentication using signed credentials with continuous authentication via a machine learning-enhanced Dirichlet trust model, enabling real-time behavioral anomaly detection. Implemented on Hyperledger family, the framework achieves 92.3% accuracy in detecting behavioral anomalies with only a 7.1% false positive rate, maintaining computational efficiency while resisting forgery and Byzantine attacks.