Detection of False Data Injection Attacks Using the Autoencoder Approach
This addresses security vulnerabilities in power grid operations, though it is incremental as it applies an existing neural network technique to a specific domain problem.
The paper tackles the problem of false data injection attacks in power systems by proposing an autoencoder-based detection method, achieving robust detection performance across various attack scenarios on the IEEE 118-bus system.
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in 'normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.