Abstract
The paper discusses an approach for detecting cyber attacks against smart power supply networks, based on identifying anomalies in network traffic by assessing its self-similarity property. Methods for identifying long-term dependence in fractal Brownian motion and real network traffic of smart grid systems are considered. It is shown that the traffic of a telecommunication network is a self-similar structure, and its behavior is close to fractal Brownian motion. Fractal analysis and mathematical statistics are used as tools in the development of this approach. The issues of a software implementation of the proposed approach and the formation of a dataset containing network packets of smart grid systems are considered. The experimental results obtained using the generated dataset have demonstrated the existence of self-similarity in the network traffic of smart grid systems and confirmed the fair efficiency of the proposed approach. The proposed approach can be used to quickly detect the presence of anomalies in the traffic with the aim of further using other methods of cyber attack detection.
Highlights
The modern electric power systems are highly developed systems with a multi-level hierarchical structure [1]
The analysis shows that the larger network traffic and the possibility a sufficiently accurate of the self-similarity the number of groups, the earlier of anomaliesdetermination can be detected, and actions needindex to be the number of groups,approach
The paper has proposed a new approach to the detection of cyberattacks in the smart grid (SG) network, based on the fractal analysis of network traffic
Summary
The modern electric power systems are highly developed systems with a multi-level hierarchical structure [1]. The energy system based on the SG concept is a single energy and information complex, where managed objects should allow remote control, and the situation assessment and emergency automation systems should reduce excessive requirements for power and information capacity reserves. The emergence of such a system is an opportunity, at the expense of new means and a new organization to control the functioning and development of the intelligent energy system, to provide new properties and new effects. These new properties and new effects are as follows: survivability; power quality [2]; the possibility of its accumulation; management of intersystem flows and the removal of unnecessary restrictions on the synchronous operation of all parts of the system; segmentation and Energies 2020, 13, 5031; doi:10.3390/en13195031 www.mdpi.com/journal/energies
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