Reducing the Impact of DoS Attack on Static and Dynamic SE Using a Deep Learning-Based Model

Abstract

Denial-of-service (DoS) attacks adversely impact the state estimation (SE) techniques used in power systems. Our contributions in this article are twofold. First, considering a longer duration DoS attack with continuous packet loss, an analysis is carried out on an IEEE 14 bus system to assess the performance of weighted least square (WLS) and cubature Kalman filter (CKF)-based hybrid SE. Second, a method to improve the performance of CKF under long-duration attacks by accurately predicting the synchrophasor measurements using convolutional neural network (CNN) and long short-term memory (LSTM) is proposed. CNN extracts relevant features/measurements from synchrophasor and RTU measurements. Using these extracted features, LSTM predicts all synchrophasor measurements. However, only the missing measurements are utilized from LSTM output in HSE during the attack. This renders the proposed method capable of dealing with attacks on any PMU channels. A comparison with the existing techniques showed improved performance of the proposed method. IEEE