BLIND-TWIN: Blockchain-Assisted LLM-Based Cds for Digital Twin-Enhanced Industrial AIoT

The interconnected and diverse nature of Digital Twin (DT)-based industrial Artificial Internet of Things (AIoT) systems exposes them to potential cyber threats and malicious activities. This paper introduces a novel framework called BLIND-Twin, which leverages blockchain, DT, and Large Language Model (LLM) technologies to address critical security and scalability challenges in industrial AIoT networks. By integrating DT technology, BLIND-Twin continuously mirrors physical environments, enabling real-time monitoring and synthetic data generation to simulate diverse threat scenarios. Data from the DT undergoes feature extraction via a Long Short-Term Memory (LSTM) Autoencoder (LSTM-AE) to extract essential temporal patterns, while the LLM enables adaptive, contextaware intrusion detection without retraining. A permissioned blockchain layer ensures data integrity, privacy, and secure logging through smart contracts, supporting automated threat response with verifiable audit trails. The framework's decentralized architecture mitigates Single Points of Failure (SPoF), addressing scalability and privacy concerns. Performance evaluations utilizing datasets like 5G-NIDD and CICIoT2023 demonstrate BLINDTwin's capability in accurately detecting various cyber threats by achieving 99.63 % accuracy with minimal latency, showcasing its effectiveness for complex industrial AIoT environments.