集成学习在电网假数据入侵检测中的应用

人工智能和机器学习的发展为入侵电网数据采集与监视控制(supervisory control and data ac-quisition,SCADA)系统的虚假数据检测,提供了新的高效解决方案.目前,针对运用机器学习中的单分类器对电网中虚假数据的检测,出现的准确率低、误检率高、模型区分能力差等问题,提出了一种基于集成学...

Application of integrated learning in the intrusion detection of power grid false data

The development of artificial intelligence and machine learning provides a new and efficient solution for the false data detection of supervisory control and data acquisition(SCADA)system.At present, using single classifier in machine learning to detect the false data in power grid has some problems, such as low accuracy, high false detection rate, poor model differentiation ability and so on. This paper proposes a detection method based on ensemble learning to binary classify the power grid data, such as gradient boosting decision tree,XGBoost,LightGBM, RF-LightGBM and so on. Bagging classifier is used as the base classifier. After Bayesian parameter adjustment, the voting strategy is used to integrate.Ensemble learning not only integrates the advantages of each classifier, but also reduces the false detection rate, and improves the detection accuracy and the stability of model distinguishing ability.The experimental results show that the algorithm has certain application and reference value in the field of data detection.