Title: Network traffic anomaly detection based on deep learning: a review

Authors: Wenjing Zhang; Xuemei Lei

Addresses: School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China ' Office of Information Construction and Management, University of Science and Technology Beijing, Beijing 100083, China

Abstract: Network traffic anomaly detection has become an important research topic with the increasing prevalence of network attacks. Deep learning, with its ability to analyse large-scale datasets, has emerged as a powerful tool for network traffic anomaly detection. This paper presents a comprehensive overview of state-of-the-art deep learning-based network traffic anomaly detection models including VAE, BiLSTM, and vision transformer, in terms of dimensional deduction, time dependence and data imbalance. The performance of these models has been evaluated and compared on KDDCUP99 and CICIDS2017 datasets. Finally, we outline challenges and future research aimed at enhancing the performance and practicality of network traffic anomaly detection based on deep learning.

Keywords: anomaly detection; deep learning; network traffic; network security.

DOI: 10.1504/IJCSE.2024.138423

International Journal of Computational Science and Engineering, 2024 Vol.27 No.3, pp.249 - 257

Received: 23 Sep 2022
Accepted: 06 May 2023
Published online: 03 May 2024 *

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