Explainable Intelligence and LLM-Based Secure Framework for Industrial Internet of Things

The Industrial Internet of Things (IIoT) is transforming the industrial landscape by enabling real-time data exchange and automation, reducing the need for human intervention and enhancing accuracy beyond manual labor. However, this interconnectedness creates a vast assortment of security vulnerabilities, ranging from device-level attacks to a complex nexus of network breaches. This paper introduces a comprehensive security framework utilizing Machine Learning (ML) for smart threat identification and Explainable AI (XAI) to improve transparency and build trust in decision-making processes. To effectively tackle the dynamic and complex challenges of IIoT environments, we introduce Large Language Models (LLMs) as an innovative and critical layer within our security pipeline. LLMs are utilized for contextual threat evaluation and support in real-time incident response. We have also demonstrated that the incorporation of ML, XAI, and LLM technologies not only boosts detection precision and speeds up response times but also improves human comprehension and operational effectiveness. The XG Boost model performed the best amongst all the ML models, achieving accuracy. We further used LIME and SHAP as the XAI tools to provide the reasoning behind the model’s prediction. The Judge and Lawyers models are implemented as a part of the LLM Layer to provide precise diagnostic information as a part of security threat detection.