基于改进生成对抗网络的虚假数据注入攻击检测方法

False Data Injection Attack Detection Method Based on Improved Generative Adversarial Network

随着新型能源互联网的发展,大规模的传感量测系统为基于数据驱动的虚假数据注入攻击检测方法提供了数据支持,然而攻击样本数据不平衡问题会影响此类方法的性能。提出了基于改进生成对抗网络(generative adversarial network,GAN)和极端随机树的数据重平衡攻击检测模型。首先,为了生成高质量数据,设计GAN的结构使其训练稳定;其次,使用Copula函数构建电力系统状态量之间的空间关联性以适应分布式能源的接入;然后,对改进的GAN进行对抗训练得到重平衡的数据集,采用极端随机树分类器实现攻击检测。此外,设计基于多种分类器的数据有效性指标评估生成数据的质量。通过对比实验对所提方法进行验证,结果表明该方法能生成高质量的量测数据,可以有效解决数据不平衡问题,攻击检测率达98.95%。

With the development of new-type energy internet,large-scale sensing measurement systems provide data support for data-driven detection of false data injection attack.However,the problem of unbalanced attack data will affect the performance of such methods.Therefore,a data rebalance attack detection model based on improved generative adversarial network(GAN)and extremely randomized tree is proposed.Firstly,the GAN structure is designed to make the training procedure stable enough to generate high-quality data.Secondly,the Copula function is used to construct the spatial correlation between the power system states to adapt to the integration of the distributed energy resources.Then,a rebalanced dataset is obtained through the adversarial training of the improved GAN,and the extremely randomized tree classifier is used to detect the attack.In addition,the data validity index based on multiple classifiers is designed to evaluate the quality of the generated data.The effect of the proposed method is verified by comparative experiments.Results show that the method can generate high-quality measurement data,solve the problem of data imbalance,and the attack detection rate is 98.95%.