THE ROLE OF ARTIFICIAL INTELLIGENCE IN DETECTING CYBERATTACKS ON ENERGY SYSTEMS

Authors: Inda Kreso

Affiliation: University of Sarajevo

Category:

Keywords: artificial intelligence, cybersecurity, cyberattacks, machine learning, energy sector

ABSTRACT. The latest trends in the energy sector demand the digitalization of energy infrastructure, which significantly enables more efficient production, distribution, transmission, and management of electricity. There are also additional security risks that come with making the energy system digital. Cyberattacks are happening more and more often on systems like SCADA and Smart Grids. An attack on energy infrastructure might put national security at risk. Artificial Intelligence (AI) is already being intensively used across various areas of the energy sector, but it’s true significance lies in whether AI algorithms can detect cyberattacks in advance and trigger timely alerts to prevent them. This paper is designed as an experimental case study with a strong literature review. A publicly available data set that is a simulation of energy network data under a cyberattack is used to train three different commonly used machine learning based AI models in order to see which one is the best at detecting a cyberattack on energy infrastructure. Three commonly used machine learning based algorithm that are used in IDS (Intrusion Detection System) were used in this research in order to comparatively test their performance of detecting cyberattacks or threats in the energy systems: Random Forest (RF), Ranger Random Forest (RANGER RF), and XGBoost. The training process and analysis were conducted in RStudio, an integrated development environment (IDE) for R programming, and the programming language was R. This research aimed not only to provide a comprehensive literature review through which gaps and shortcomings in the existing body of work were identified, but also to address a clearly visible gap in the literature, the lack of concrete applications of AI methods for detecting cyberattacks on energy infrastructure, by implementing a practical example using AI models on a dataset simulating cyberattacks on an energy network. This research concludes that RANGER RF algorithm (optimized version of Random Forest) proves to be the most efficient one. RANGER RF has the best performance when it comes to detecting cyberattacks on energy infrastructure and the most suitable as a part of Intrusion Detection Systems (IDS).

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