A Digital Warning System for False Data Injection Attacks in the Power Grid Based on Machine Learning

In response to the security risks and economic losses caused by false data injection attacks on the current power grid (such attacks interfere with the operation of the power grid by tampering with sensor data), this article proposes a digital warning system for power grid false data injection attacks based on the currently best performing deep learning algorithm. Firstly, the article adopts an advanced method combining Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) to deeply extract and analyze the temporal and spatial features of power grid data, in order to accurately capture potential abnormal behaviors; next, the article utilizes self attention mechanism to optimize the feature selection ability of the model, thereby effectively improving the detection accuracy and anti-interference ability of the model; finally, the article further enhances the robustness and generalization performance of the system through ensemble learning strategies, in order to adapt to different types of attack scenarios and data features. In the experiment, the AUC value of the system reaches 0.92, with an accuracy rate of 92% and a recall rate of 90%. The anti-interference ability is also significantly improved under different noise intensities, especially maintaining an accuracy rate of 85% at a noise intensity of 0.3. These test results show that the suggested method is reliable and feasible in intricate power grid settings.