Research and Application Analysis of Deep Learning-based Network Malicious Traffic Detection Methods

DOI

10.2991/978-94-6463-823-3_5How to use a DOI?

Keywords

Deep Learning; Malicious Traffic; Multimodal

Abstract

Malicious traffic detection is an important topic of research today. Researchers have found a variety of detection methods and all have better performance, however, with the rapid development of the network, a series of updated iteration of the new attack means continuously invade the network system. The countermeasures and summaries of the existing methods have not yet reached a comprehensive coverage of the literature, so this paper analyses four malicious traffic detection methods that have been studied in recent years: convolutional neural network, Convolutional Neural Network with Long and Short-Term Memory Network, derived Auto-Encoder patterns, and multimodal methods. Different application scenarios are also analysed. Each method has retrieved several literatures for analysis and finally concluded that these models have shown high detection accuracy in their respective applicable scenarios. This paper focuses on a brief evaluation of each type of model by analysing its functional working principle, model validation, generalisation ability, overall strengths and weaknesses.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yihe Zhang
PY  - 2025
DA  - 2025/08/31
TI  - Research and Application Analysis of Deep Learning-based Network Malicious Traffic Detection Methods
BT  - Proceedings of the 2025 3rd International Conference on Image, Algorithms, and Artificial Intelligence (ICIAAI 2025)
PB  - Atlantis Press
SP  - 44
EP  - 57
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-823-3_5
DO  - 10.2991/978-94-6463-823-3_5
ID  - Zhang2025
ER  -