AI-Driven Framework for Industrial Internet Security Protection
DOI: https://doi.org/10.62381/O242B06
Author(s)
Dan Wang1, Lijian Wang2
Affiliation(s)
1Shandong Labor Vocational and Technical College, Shandong, China
2Shandong Special Inspection Group Lu’an Engineering Technology Services Co., Ltd, Shandong, China
Abstract
The widespread adoption of Artificial Intelligence (AI) presents both opportunities and challenges for industrial internet security. This study investigates the pivotal role and methodologies of AI in constructing a robust industrial internet security framework, aiming to provide comprehensive protection for the intelligent and interconnected industrial systems. By systematically analyzing current security threats and technical demands in the industrial internet, a novel AI-based security framework is proposed, encompassing four core modules: threat detection, risk assessment, real-time response, and recovery. Leveraging deep learning, federated learning, and knowledge graph technologies, a multi-layered collaborative security governance mechanism is designed to address the complexities of industrial scenarios. Simulation experiments validate the framework’s effectiveness and adaptability. Results demonstrate that AI significantly enhances security capabilities by improving anomaly detection accuracy, reducing response time, and optimizing overall security management efficiency. This research offers theoretical foundations and technological pathways for AI-driven industrial internet security, with substantial academic and practical implications.
Keywords
Artificial Intelligence; Industrial Internet; Cybersecurity; Deep Learning; Federated Learning
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