Artificial Intelligence-Based Cybersecurity Monitoring Solutions in Industrial Networks: A Literature Review
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Abstract
The convergence of operational technologies (OT) with information technologies (IT) has significantly increased the risk of industrial networks suffering from cyber-attacks. The objective of this article has been to systematically review the existing literature on cybersecurity monitoring solutions in industrial networks based on artificial intelligence (AI), with the purpose of identifying the main manufacturers, solutions, functionalities, and industrial sectors where this technology is applied. The PRISMA method has been used to conduct a systematic search for documentation containing relevant information in the last 7 years. The results obtained show that there are manufacturers such as Nozomi Networks, Claroty, Dragos, and Darktrace, which have AI-based cybersecurity monitoring solutions. These solutions have functionalities such as asset and communication identification, behavior analysis, vulnerability management, and threat intelligence. It is also identified that these technologies are being applied in different industrial sectors, such as energy, oil and gas, water and sanitation, among others. It is concluded that the adoption of these type of technologies is of vital importance for the faster and more accurate detection of cyber threats in critical infrastructures, which is why it is important to continue investing in the development and application of these solutions.
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Lenin Hernán Cortés-Llanganate, Universidad Católica de Cuenca - Ecuador
https://orcid.org/0009-0006-4904-5244
Computer Systems Engineer. Holds several certifications in the field of cybersecurity and currently works as a Cybersecurity Coordinator at Radical CIA. LTDA. Has experience in leading cybersecurity projects and consultancies both inside and outside of Ecuador
Andrés Sebastián Quevedo-Sacoto, Universidad Católica de Cuenca - Ecuador
https://orcid.org/0000-0001-5585-0270
Sebastián Quevedo is a doctoral student in Applied Computer Science (PhD) from the Escuela Superior Politécnica del Litoral and holds a master's degree in Geomatics from the University of Cuenca. Sebastián graduated as a Systems Engineer from the Universidad Politécnica Salesiana.
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