500 rub
Journal Radioengineering №2 for 2026 г.
Article in number:
The use of artificial neural networks to detect malicious software in radio engineering systems based on Linux operating systems
Type of article: scientific article
DOI: https://doi.org/10.18127/j00338486-202602-10
UDC: 004.05
Authors:

N.N. Samarin, E.Y. Pavlenko, D.S. Lavrova, E.A. Abramov

Abstract:

The article proposes a method for detecting malware on widespread complex systems running Linux family operating systems. Experimental studies confirmed the effectiveness of the method: the best accuracy was 94.5%.

Pages: 73-84
References
  1. Exploits and vulnerabilities in Q1 2025 [Jelektronnyj resurs]. URL: https://securelist.com/vulnerabilities-and-exploits-in-q1-2025/116624/ (data obrashhenija 20.10.2025).
  2. Linux malware: types, families and trends [Jelektronnyj resurs]. URL: https://any.run/cybersecurity-blog/linux-malware-types-families-and-trends/ (data obrashhenija 20.10.2025).
  3. How to identify and remove linux malware infections [Jelektronnyj resurs]. URL: https://www.itprotoday.com/linux-os/how-to-identify-and-remove-linux-malware-infections (data obrashhenija 20.10.2025).
  4. What are indicators of compromise (IoC) [Jelektronnyj resurs]. URL: https://www.cloudflare.com/ru-ru/learning/security/what-are-indicators-of-compromise/ (data obrashhenija 20.10.2025).
  5. Onyebuchi O.B. Signature based network intrusion detection system using feature selection on android. Signature. 2020. V. 11. № 6. P. 551-558.
  6. Punyasiri D.L.S. Signature & behavior based malware detection. Malabe. Sri Lanka: Sri Lanka Institute of Information Technology. 2023.
  7. Sheluhin O.I., Rybakov S.Ju., Zvezhinskij S.S. Obnaruzhenie kiberatak i vredonosnogo programmnogo obespechenija nulevogo dnja metodami mashinnogo obuchenija. Radiotehnika. 2025. T. 89. № 8. S. 184-198. DOI: https://doi.org/10.18127/j00338486-202508-21 (in Russian).
  8. Qamar R., Zardari B.A. Artificial neural networks: an overview. Mesopotamian Journal of Computer Science. 2023. V. 2023. P. 124-133.
  9. Appalonov A.M., Maslennikova Ju.S., Sherstjukov O.N. Primenenie nejronnyh setej glubokogo obuchenija dlja analiza prostranstvennyh i vremennýh komponent razlozhenija polnogo jelektronnogo soderzhanija ionosfery. Radiotehnika. 2025. T. 89. № 1. S. 172-179. DOI: https://doi.org/10.18127/j00338486-202501-16 (in Russian).
  10. Malygin I.V., Bel'kov S.A., Mihajlik D.A., Stafeev K.V. Razrabotka metodov formirovanija obrazcov, obuchajushhih nej-ronnuju set' obnaruzhivat' i klassificirovat' pomehi v strukture poleznogo signala. Radiotehnika. 2024. T. 88. № 6. S. 121-129. DOI: 10.18127/j00338486-202406-15 (in Russian).
  11. Montesinos López O.A., Montesinos López A., Crossa J. Fundamentals of artificial neural networks and deep learning. Multivariate statistical machine learning methods for genomic prediction. Cham: Springer International Publishing. 2022. P. 379-425.
  12. A Brief introduction to recurrent neural networks [Jelektronnyj resurs]. URL: https://towardsdatascience.com/a-brief-introduction-to-recurrent-neural-networks-638f64a61ff4/ (data obrashhenija 11.05.2025).
  13. Taye M.M. Theoretical understanding of convolutional neural network: Concepts, architectures, applications, future directions. Computation. 2023. V. 11. № 3. P. 52.
  14. Zhao X. et al. A review of convolutional neural networks in computer vision. Artificial Intelligence Review. 2024. V. 57. № 4. P. 99.
  15. Dosovitskiy A. et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. 2020.
  16. Zhang M. Neural attention: Enhancing qkv calculation in self-attention mechanism with neural networks. arXiv preprint arXiv:2310.11398. 2023.
  17. Bensaoud A., Kalita J. Deep multi-task learning for malware image classification. Journal of Information Security and Applications. 2022. V. 64. P. 103057.
  18. Katar O., Yıldırım O. Classification of Malware Images Using Fine-Tunned ViT. Sakarya University Journal of Computer and Information Sciences. 2024. V. 7. № 1. P. 22-35.
  19. Malware Bazaar Database [Jelektronnyj resurs]. URL: https://bazaar.abuse.ch/browse/tag/elf/ (data obrashhenija 11.05.2025).
  20. Labeled-Elfs [Jelektronnyj resurs]. URL: https://github.com/nimrodpar/Labeled-Elfs/tree/main (data obrashhenija 11.05.2025).
  21. Linux-malware [Jelektronnyj resurs]. URL: https://github.com/timb-machine/linux-malware/tree/main/malware/binaries (data obrashhenija 11.05.2025).
  22. Linux-Malware-Samples [Jelektronnyj resurs]. URL: https://github.com/MalwareSamples/Linux-Malware-Samples (data obrashhenija 11.05.2025).
  23. Assemblage Dataset [Jelektronnyj resurs]. URL: https://assemblage-dataset.net/ (data obrashhenija 11.05.2025).
  24. MalwareDatabaseUnsorted [Jelektronnyj resurs]. URL: https://github.com/Pyran1/MalwareDatabaseUnsorted (data obrashhenija 11.05.2025).
  25. Kalyan E.V.P. et al. Detection of malware using CNN. 2022 Second International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE. 2022. P. 1-6.
  26. Chaymae E.Y., Khalid C. Android malware detection through CNN ensemble learning on grayscale images. International Journal of Advanced Computer Science & Applications. 2025. V. 16. № 1.
  27. Seneviratne S. et al. Self-supervised vision transformers for malware detection. IEEE Access. 2022. V. 10. P. 103121-103135.
  28. Bavishi S., Modi S. Accelerating malware classification: a vision transformer solution. arXiv preprint arXiv:2409.19461. 2024.
Date of receipt: 29.12.2025
Approved after review: 13.01.2026
Accepted for publication: 28.01.2026