Artificial intelligence-powered computer networks for cyberattack protection: a systematic literature review
DOI:
https://doi.org/10.55873/rad.v4i2.373Keywords:
machine learning, educational cybersecurity, intrusion detection, neural networks, technological vulnerabilitiesAbstract
In the context of rising cyberattacks and the vulnerability of educational computer networks, this study conducted a systematic review to assess the effectiveness of artificial intelligence (AI) compared to traditional protection methods. Following PRISMA guidelines, 17 studies published between 2020 and 2024 were analyzed from databases such as IEEE Xplore and Scopus, with inclusion criteria focused on temporal relevance and educational networks. The findings revealed that AI achieved an average detection accuracy of 89%, outperforming traditional approaches at 72%, particularly in identifying emerging threats. However, key barriers were identified, including high implementation costs (averaging USD 28,000 in hardware), lack of specialized personnel, and heterogeneity across educational infrastructures. The study concludes that collaborative models, low-code tools, and hybrid approaches are essential to enable AI adoption in resource-constrained contexts. Furthermore, it highlights the importance of extending future research to diverse geographical settings to strengthen the applicability and scalability of AI in educational cybersecurity.
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Copyright (c) 2025 Jhenrry Rivera-Moreto, Yngue Elizabeth Ramírez-Pezo
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