POTENTIAL USE OF NEURAL NETWORKS TO DETECT ANOMALIES INTRUSIONS IN NETWORK TRAFFIC

Authors

DOI:

https://doi.org/10.30890/2709-1783.2023-26-01-008

Keywords:

cybersecurity of an information system, intrusion into an information system, intrusion detection, machine learning, neural network

Abstract

The rapid digitalization of the world has led to various attacks on computer systems and networks, so cybersecurity of networks is an extremely important and relevant component of information security today. In our study, we compare two deep learning mode

References

A Convolutional Neural Network for Network Intrusion Detection System / L. Mohammadpour et al. Barcelona, 24–26 October 2018. 2018. P. 50–55.

LSTM learning with bayesian and gaussian processing for anomaly detection in industrial IoT/ D. Wu et al. IEEE transactions on industrial informatics. 2020. Vol. 16, no. 8. P. 5244–5253. URL: https://doi.org/10.1109/tii.2019.2952917 (date of access: 15.04.2023).

Gradient-based learning applied to document recognition / Y. Lecun et al. Proceedings of the IEEE. 1998. Vol. 86, no. 11. P. 2278–2324. URL: https://doi.org/10.1109/5.726791 (date of access: 15.03.2023).

I. Cvitić, D. Perakovic, B. B. Gupta and K. -K. R. Choo, "Boosting-Based DDoS Detection in Internet of Things Systems," in IEEE Internet of Things Journal, vol. 9, no. 3, pp. 2109-2123, 1 Feb.1, 2022, doi: 10.1109/JIOT.2021.3090909.

Staudemeyer R. C. Applying long short-term memory recurrent neural networks to intrusion detection. South African Computer Journal. 2015. Vol. 56. URL: https://doi.org/10.18489/sacj.v56i1.248 (date of access: 15.04.2023).

Young T., Nammous M. K., Saeed K. Advanced Computing and Systems for Security. Berlin, Germany: Springer; 2019. Natural language processing: speaker, language, and gender identification with LSTM; pp. 143–156.

Published

2023-04-30

How to Cite

Haidur, H., Gakhov, S., & Bryhynets, A. (2023). POTENTIAL USE OF NEURAL NETWORKS TO DETECT ANOMALIES INTRUSIONS IN NETWORK TRAFFIC. SWorld-Ger Conference Proceedings, 1(gec26-01), 16–19. https://doi.org/10.30890/2709-1783.2023-26-01-008