Methodology for Management of the Protection System of Smart Power Supply Networks in the Context of Cyberattacks

Igor Kotenko,Mikhail Karpov,Igor Saenko,Oleg Lauta

doi:10.3390/en14185963

Abstract

This paper examines an approach that allows one to build an efficient system for protecting the information resources of smart power supply networks from cyberattacks based on the use of graph models and artificial neural networks. The possibility of a joint application of graphs, describing the features for the functioning of the protection system of smart power supply networks, and artificial neural in order to predict and detect cyberattacks is considered. The novelty of the obtained results lies in the fact that, on the basis of experimental studies, a methodology for managing the protection system of smart power supply networks in conditions of cyberattacks is substantiated. It is based on the specification of the protection system by using flat graphs and implementing a neural network with long short-term memory, which makes it possible to predict with a high degree of accuracy and fairly quickly the impact of cyberattacks. The issues of software implementation of the proposed approach are considered. The experimental results obtained using the generated dataset confirm the efficiency of the developed methodology. It is shown that the proposed methodology demonstrates up to a 30% gain in time for detecting cyberattacks in comparison with known solutions. As a result, the survivability of the Self-monitoring, Analysis and Reporting technology (SMART) grid (SG) fragment under consideration increased from 0.62 to 0.95.

Highlights

The use of the SMART grid makes it possible to reduce the cost of the electrical network, solve the problem of technological limitation of electricity when consumed near peak capacities, use a large number of renewable energy sources, and switch from a centralized topology of the electrical network to a highly distributed topology
The analysis showed that one of the most efficient prediction methods is the usage of artificial neural networks with long short-term memory (LSTM)
To solve the problem associated with predicting and detecting a cyberattack, it is very important to determine machine learning algorithms or neural networks, and to highlight the parameters that are most susceptible to anomalous deviation during the course of the attacker’s influence

Summary

Introduction

Application of open information and communication networks, protocols and technologies for collecting information on energy production and energy consumption; Active bidirectional scheme of interaction in real time of the information exchange between all elements and participants of the network (from power generators to terminal devices of power consumers); Coverage of the entire technological chain of the electric power system from energy producers and power distribution networks to the end consumers; Constant exchange between the network elements of information about the parameters of electrical energy, modes of consumption and generation, the amount of energy consumed and planned consumption, and commercial information [1]. The use of the SMART grid makes it possible to reduce the cost of the electrical network, solve the problem of technological limitation of electricity when consumed near peak capacities, use a large number of renewable energy sources, and switch from a centralized topology of the electrical network to a highly distributed topology. The greatest danger is caused by cyberattacks

Results

Discussion

Conclusion