A spatio-temporal feature learning method based on transformer

Liuquan Xu; Xuefeng Jiang; Li Yu

doi:10.1117/12.3038729

22 July 2024 A spatio-temporal feature learning method based on transformer

Liuquan Xu, Xuefeng Jiang, Li Yu

Proceedings Volume 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024); 132220H (2024) https://doi.org/10.1117/12.3038729
Event: Third International Conference on Signal Processing and Communication Security (ICSPCS 2024), 2024, Chengdu, China

Abstract

With the development of the network world, people's lives are gradually becoming intelligent. However, there are also crises in the network, such as attacks by some illegal hackers through a variety of complex means. The emergence of intrusion detection systems has greatly protected the network environment. However, traditional techniques cannot sufficiently learn the characteristics of attack traffic, resulting in poor performance. In order to address the limitations of traditional models, this paper proposes a transformer-based spatio-temporal feature learning method called STF-Transformer. It studies the temporal and spatial characteristics of the attack traffic through improved encoders and decoders, respectively. It greatly enhances the detection capability of the model. To demonstrate the effectiveness of the STF-transformer method. We measured the performance of the STF-transformer method on the CICIDS2017 and NSL-KDD datasets. The experimental results demonstrated that the STF-transformer method improved the detection rate. It achieved satisfactory results in terms of accuracy, F1 score, and other metrics.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Liuquan Xu, Xuefeng Jiang, and Li Yu "A spatio-temporal feature learning method based on transformer", Proc. SPIE 13222, International Conference on Signal Processing and Communication Security (ICSPCS 2024), 132220H (22 July 2024); https://doi.org/10.1117/12.3038729

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