A Data-Driven Dynamic State Estimation for Smart Grids under DoS Attack using State Correlations

The denial-of-service (DoS) attack is a very common type of cyber attack that can affect critical cyber-physical systems, such as smart grids, by hampering the monitoring and control of the system, for example, creating unavailability of data from the attacked zone. While developing countermeasures can help reduce such risks, it is essential to develop techniques to recover from such scenarios if they occur by estimating the state of the system. Considering the continuous data-stream from the PMUs as time series, this work exploits the bus-to-bus cross-correlations to estimate the state of the system's components under attack using the PMU data of the rest of the buses. By applying this technique, the state of the power system can be estimated under various DoS attack sizes with great accuracy. The estimation accuracy in terms of the mean squared error (MSE) has been used to identify the relative vulnerability of the PMUs of the grid and the most vulnerable time for the DoS attack.