Detection of False Data Injection Attacks Using the Autoencoder Approach

Abstract

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.

arXiv comment6 pages, 5 figures, 1 table, conference

References in corpus (3)

• Adam: A Method for Stochastic Optimization
• From Static to Dynamic Anomaly Detection with Application to Power System Cyber Security
• Training Strategies for Autoencoder-based Detection of False Data Injection Attacks

Cited by in corpus (7)

• Generating Multivariate Load States Using a Conditional Variational Autoencoder
• Machine Learning in Generation, Detection, and Mitigation of Cyberattacks in Smart Grid: A Survey
• ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems
• A Deep Learning based Detection Method for Combined Integrity-Availability Cyber Attacks in Power System
• Cybersecurity for Modern Smart Grid against Emerging Threats
• Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids
• Training Strategies for Autoencoder-based Detection of False Data Injection Attacks