Szczegóły publikacji
Opis bibliograficzny
Cybersecurity challenges and opportunities of machine learning-based artificial intelligence / Paweł Czaja, Bartłomiej GDOWSKI, Marcin NIEMIEC, Wim Mees, Nikolai Stoianov, Konstantinos Votis, Vyacheslav Kharchenko, Vasilis Katos, Matteo Merialdo // Neural Computing & Applications ; ISSN 0941-0643. — 2025 — vol. 37 iss. 33, s. 27931–27956. — Bibliogr. s. 27952–27956, Abstr. — Publikacja dostępna online od: 2025-10-08
Autorzy (9)
- AGHCzaja Paweł
- AGHGdowski Bartłomiej
- AGHNiemiec Marcin
- Mees Wim
- Stoianov Nikolai
- Votis Konstantinos
- Kharchenko Vyacheslav
- Katos Vasilis
- Merialdo Matteo
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164421 |
|---|---|
| Data dodania do BaDAP | 2025-11-27 |
| Tekst źródłowy | URL |
| DOI | 10.1007/s00521-025-11604-9 |
| Rok publikacji | 2025 |
| Typ publikacji | przegląd |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Neural Computing & Applications |
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.
