SIR-Aided Secure Transmission and Attack Detection for Security Management of Nonlinear Cyber-Physical System Using GRU Autoencoder

This article designs a data-driven unsupervised defense scheme for nonlinear systems by proposing a machine learning approach called gate recurrent unit-based modified denoising and stable image representation-aided autoencoders. The proposed scheme decomposes original data into two subspaces through orthogonal projection. For secure transmission, information related to the system's dynamics, which is in the image space of the controlled system, is hidden through filtering, whereas only the dynamic-independent information is plaintext for transmission, which supplements the cryptographic encryption methods from a control perspective. Moreover, attack detection for nonstealthy and stealthy attacks is achieved simultaneously under the same framework. A case study is conducted for validation on the a hardware-in-the-loop platform with a mecanum-wheeled vehicle. The comparative experiments with well-known unsupervised data-driven methods show the high detection accuracy of the proposed defense scheme for nonstealthy and stealthy attacks and the excellent encryption capability.

Automated summary

This article proposes a data-driven unsupervised defense scheme for nonlinear systems, which decomposes data into two subspaces and achieves secure transmission by hiding dynamic-related information and transmitting dynamic-independent information plaintext. The scheme can detect both nonstealthy and stealthy attacks simultaneously, and comparative experiments demonstrate its high detection accuracy and excellent encryption capability.