Visible to the public Kalman Filter with Diffusion Strategies for Detecting Power Grid False Data Injection Attacks

TitleKalman Filter with Diffusion Strategies for Detecting Power Grid False Data Injection Attacks
Publication TypeConference Paper
Year of Publication2017
AuthorsJiang, Y., Hui, Q.
Conference Name2017 IEEE International Conference on Electro Information Technology (EIT)
Date Publishedmay
ISBN Number978-1-5090-4767-3
Keywordscompositionality, Computer crime, control system signals, Detectors, diffusion Kalman filter, diffusion strategies, distributed estimation models, distributed Kalman filtering, distributed network, electronic power grid, Estimation, estimation theory, Human Behavior, human factors, Kalman filters, Meters, power engineering computing, power grid false data injection attacks, power grid states estimation, power grid states measurement, power system measurement, power system security, power system state estimation, pubcrawl, resilience, Resiliency, security of data, Sensor networks, sensor readings, Smart Grid Sensors, Smart grids, smart meters, smart power grids, state estimation models

Electronic power grid is a distributed network used for transferring electricity and power from power plants to consumers. Based on sensor readings and control system signals, power grid states are measured and estimated. As a result, most conventional attacks, such as denial-of-service attacks and random attacks, could be found by using the Kalman filter. However, false data injection attacks are designed against state estimation models. Currently, distributed Kalman filtering is proved effective in sensor networks for detection and estimation problems. Since meters are distributed in smart power grids, distributed estimation models can be used. Thus in this paper, we propose a diffusion Kalman filter for the power grid to have a good performance in estimating models and to effectively detect false data injection attacks.

Citation Keyjiang_kalman_2017