Detection of false data injection in electric energy metering platforms using gradient lifting decision trees and MLP neural networks

This study investigates a false data injection detection method in an automatic data acquisition platform for electric energy measurement with the aim of ensuring the stability and security of the power system. A fake data injection detector model, including a predictor and discriminator, was constructed. The predictor was based on a gradient boosting decision tree to predict potential data injection anomalies. The discriminator used a multilayer perceptron (MLP) neural network, combined with difference analysis between the predicted and actual values, to determine false data injection. The improved Cauchy mutation grey Wolf optimization algorithm is used to optimize the model training to improve the detection accuracy. Experimental results show that the proposed method can efficiently process a large number of packets per second, the ROC curve performance is excellent, the detection accuracy is more than 99.97%, and the delay is less than 0.04 s. This research provides an effective false data injection detection technology for an electric energy measurement data collection platform, significantly enhances the security protection ability of the system, and has important practical application value.