Enhanced Cyber-Attack Detection in Intelligent Motor Drives: A Transfer Learning Approach With Convolutional Neural Networks

As networked digital control units become increasingly prevalent in intelligent motor drive systems, cybersecurity concerns have risen, leading to the development of various cyber-attack detection methods to improve system reliability. Although data-driven methods offer advantages over physics-based approaches, the requirement for extensive experimental data presents a significant challenge. This article proposes a novel cyber-attack detection approach for motor drives using Transfer learning based on convolutional neural networks (CNNs). The method initially pretrains a CNN model with substantial simulation data and fine-tunes it using transfer learning with limited experimental data, achieving outstanding detection performance with 99.5% accuracy while reducing development costs, risks, and time. In addition, the proposed model maintains satisfactory detection accuracy of over 96% even when experimental training data are limited to 10% of original available data. The findings indicate that transfer-learned models exhibit faster convergence and better performance when limited experimental data are available compared with newly-trained models. The proposed approach substantially reduces the reliance on large quantities of experimental data during the development process, lowers costs, and risks associated with cyber-attack detector development, strengthens the connections between simulations and experiments, and significantly shortens the development period by leveraging powerful simulation models.