基于深度信念网络和迁移学习的隐匿FDI攻击入侵检测

Stealthy FDI attack detection based on deep belief network and transfer learning

成功地检测隐匿虚假数据入侵(false data injection,FDI)攻击是确保电力系统安全运行的关键.然而,大多数工作通过建立FDI攻击模型模拟真实的入侵行为,得到的模拟数据往往与真实数据存在一定的差异,导致基于机器学习的检测方法出现较差的学习效果.鉴于此,针对源域中模拟样本数据量大而目标域中真实样本标记少的特点,提出基于深度信念网络(DBN)和迁移学习的检测算法.DBN中的受限玻尔兹曼机(restrict boltzmann machine,RBM)能够对海量目标域无标签样本进行特征自学习,基于模型的迁移学习方法可以克服数据之间的差异性,同时解决有标签真实样本稀缺的问题.最后,在IEEE 14-bus电力系统模型上验证了所提出方法的优点和有效性.

Successful detection of false data injection(FDI)attacks are essential for ensuring secure power grids operation.However,most work simulates real intrusion behaviors by establishing FDI attack models,and the simulated data obtained is often different from the real data,resulting in poor learning effects based on machine learning detection methods.Motivated by this fact,considering the large amount of simulated sample data in the source domain and a small number of labeled real samples in the target domain,a detection algorithm based on the deep belief network(DBN)and transfer learning is proposed.The restrict boltzmann machine(RBM)in the DBN can automatically extract features from a large number of unlabeled samples in the target domain,and the model-based transfer learning method overcomes the differences between data and solves the problem of the scarcity of labeled real samples.Finally,the IEEE 14-bus power system is employed to show the advantages and effectiveness of the proposed method.