Cybersecurity challenges and opportunities of machine learning-based artificial intelligence

Abstract Artificial intelligence, machine learning, and cybersecurity are the topics of discussion of contemporary information technology sector and computing research. This study investigates the integration of machine learning-based artificial intelligence in the context of cybersecurity. This paper presents an overview of the recent literature, focusing on selected popular areas related to the challenges and opportunities that such implementations introduce. The authors also assess how selected problems related to the application of machine learning algorithms affect the real effectiveness represented by the resulting models. To support this analysis, an experimental study was conducted using a real-world cybersecurity system. This demonstration illustrates the practical implementation of a machine learning-based software solution in cybersecurity and highlights the potential challenges encountered during such implementations.

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Publication Details

Journal
Neural Computing and Applications
Published
2025-10-08
DOI
https://doi.org/10.1007/s00521-025-11604-9
Citations
1
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
1.49
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Cybersecurity challenges and opportunities of machine learning-based artificial intelligence

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Neural Computing and Applications
Network Security and Intrusion Detection
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Cybersecurity challenges and opportunities of machine learning-based artificial intelligence

Wim Mees, P. Czaja, Matteo Merialdo, Vasilios Katos, Bartłomiej Gdowski, Vyacheslav Kharchenko, Nikolai Stoianov, Konstantinos Votis, Marcin Niemiec
article en
1 citations

Abstract

Abstract Artificial intelligence, machine learning, and cybersecurity are the topics of discussion of contemporary information technology sector and computing research. This study investigates the integration of machine learning-based artificial intelligence in the context of cybersecurity. This paper presents an overview of the recent literature, focusing on selected popular areas related to the challenges and opportunities that such implementations introduce. The authors also assess how selected problems related to the application of machine learning algorithms affect the real effectiveness represented by the resulting models. To support this analysis, an experimental study was conducted using a real-world cybersecurity system. This demonstration illustrates the practical implementation of a machine learning-based software solution in cybersecurity and highlights the potential challenges encountered during such implementations.

Neural Computing and ApplicationsVol. 37(33)
Jagiellonian University (PL), Royal Military Academy (BE), National Aerospace University – Kharkiv Aviation Institute (UA), IQVIA (United Kingdom) (GB), "Professor Tsvetan Lazarov" Defence Institute (BG), Information Technologies Institute (GR), Centre for Research and Technology Hellas (GR), AGH University of Krakow (PL), Bournemouth University (GB)
Openalex Percentile: Top 12%
Network Security and Intrusion Detection
1.49
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Cybersecurity challenges and opportunities of machine learning-based artificial intelligence — Wim Mees, P. Czaja, et al. · Neural Computing and Applications (2025) | TGRS Research Map | TGRS