Vulnerability Assessment of 6G-Enabled Smart Grid Cyber-Physical Systems

Next-generation wireless communication and networking technologies, such as sixth-generation (6G) networks and software-defined Internet of Things (SDIoT), make cyber-physical systems (CPSs) more vulnerable to cyberattacks. In such massively connected CPSs, an intruder can trigger a cyberattack in the form of false data injection, which can lead to system instability. To address this issue, we propose a graphics-processing-unit-enabled adaptive robust state estimator. It comprises a deep learning algorithm, long short-term memory, and a nonlinear extended Kalman filter, and is called LSTMKF. Through an SDIoT controller, it provides an online parametric state estimate. The reliability is improved by performing two levels of online parametric state estimation for secure communication and load management. The CPS under study is a 6G and SDIoT-enabled smart grid, which is tested on IEEE 14, 30, and 118 bus systems. Compared to existing techniques, the proposed algorithm is able to estimate the state variables of the system even during or after a cyberattack, with lower time complexity and high accuracy.

Automated summary

The proposed graphics-processing-unit-enabled adaptive robust state estimator aims to address the vulnerability of cyber-physical systems to cyberattacks caused by next-generation wireless communication and networking technologies. It provides online parametric state estimation through two levels of estimation, improving system reliability for secure communication and load management. The algorithm demonstrates lower time complexity and higher accuracy in estimating system state variables during or after cyberattacks compared to existing techniques.