The article discusses an approach to the construction and operation of a proactive system for protecting smart power grids against cyberattacks on service data transfer protocols. It is based on a combination of computational intelligence methods: identifying anomalies in network traffic by evaluating its self-similarity, detecting and classifying cyberattacks in anomalies, and taking effective protection measures using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells. Fractal analysis, mathematical statistics, and neural networks with long short-term memory are used as tools in the development of this protection system. The issues of software implementation of the proposed system and the formation of a data set containing network packets of a smart grid system are considered. The experimental results obtained using the generated data set demonstrated and confirmed the high efficiency of the proposed proactive smart grid protection system in detecting cyberattacks in real or near real-time, as well as in predicting the impact of cyberattacks and developing efficient measures to counter them.
Keywords: Hurst exponent; cyberattacks; fractal analysis; fractal dimension; scaling exponent; time series.