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A Proactive Protection of Smart Power Grids against Cyberattacks on Service Data Transfer Protocols by Computational

Igor Kotenko¹, Igor Saenko¹, Oleg Lauta²

¹Laboratory of Computer Security Problems, St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 39, 14th Liniya, 199178 St. Petersburg, Russia.

Sensors (Basel, Switzerland)

|October 14, 2022

Summary

This summary is machine-generated.

This study introduces a proactive system using computational intelligence to defend smart power grids against cyberattacks. It effectively detects, classifies, and counters threats in real-time, enhancing grid security.

Keywords:

Hurst exponent cyberattacks fractal analysis fractal dimension scaling exponent time series

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Area of Science:

Cybersecurity
Computer Science
Electrical Engineering

Background:

Smart power grids are increasingly vulnerable to cyberattacks targeting data transfer protocols.
Existing protection systems may lack the proactivity and real-time response needed for sophisticated threats.

Purpose of the Study:

To develop and evaluate a proactive system for smart power grid protection against cyberattacks.
To enhance the resilience of smart grids by integrating computational intelligence for threat detection and mitigation.

Main Methods:

Utilizing computational intelligence, including fractal analysis and mathematical statistics, to evaluate network traffic self-similarity for anomaly detection.
Employing neural networks, specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) cells, for cyberattack classification and proactive defense strategy development.
Developing a software implementation and generating a dedicated dataset of smart grid network packets for system validation.

Main Results:

The proposed system demonstrated high efficiency in detecting cyberattacks in near real-time.
The system successfully predicted the impact of cyberattacks and formulated effective countermeasures.
Experimental results confirmed the system's capability in proactive threat identification and response.

Conclusions:

The developed proactive system offers a robust solution for safeguarding smart power grids against cyber threats.
The integration of LSTM and GRU cells provides advanced capabilities for anomaly detection and attack mitigation.
This approach significantly improves the security and reliability of smart grid infrastructure.