Gaussian Filtering with False Data Injection and Randomly Delayed Measurements

State estimation in cyber-physical systems is a challenging task involving integrating physical models and measurements to estimate dynamic states accurately practical machine-to-machine IoT deployments. However, advanced wireless communication intelligent has increased vulnerability of external intrusion through centralized server. This study addresses the challenge Gaussian filtering for specific type stochastic nonlinear system vulnerable cyber attacks delayed measurements. These occur randomly when data transmitted from sensor nodes remote filter nodes. To address this issue, new attack model proposed that combines false injection measurement into unified framework. The also analyzes stability establishes sufficient conditions ensure error remains bounded even presence occurring methodology demonstrated compared with other widely used approaches using simulated highlight its effectiveness usefulness.