Artificial intelligence-driven security framework for internet of things-enhanced digital twin networks

Abstract

In the evolving area of internet of things (IoT)-enabled digital twin networks (DTNs), ensuring robust security and data privacy is a necessity. This paper presents an AI-driven security framework designed to address security and privacy requirements in DTNs by integrating advanced machine learning techniques. We propose a novel approach that combines long short-term memory (LSTM) networks with transfer learning and differential privacy (DP) to enhance threat detection and preserve sensitive data. The LSTM networks are employed to model sequential data patterns, crucial for identifying and mitigating security threats in such dynamic environments. In addition, transfer learning is utilized to leverage pre-trained models, improving accuracy and reducing training time while DP is incorporated to protect user privacy by introducing the Gaussian noise into the training process, thereby ensuring confidential data handling. We formulate the proposed AI-driven security solution as a multi-layer framework and investigate its ability to achieve significant improvements, in terms of detection accuracy and privacy preservation, compared to conventional methods. We then obtain simulation results to demonstrate the effectiveness of the solution in adapting to evolving threats while maintaining high-performance standards. It is believed that the proposed solution will open new research directions towards improving security in emerging cyber–physical systems such as DTNs.