CLC number:
On-line Access: 2025-07-28
Received: 2024-05-30
Revision Accepted: 2024-09-18
Crosschecked: 2025-07-30
Cited: 0
Clicked: 944
Citations: Bibtex RefMan EndNote GB/T7714
Jue CHEN, Wanxiao LIU, Xihe QIU, Wenjing LV, Yujie XIONG. CRGT-SA: an interlaced and spatiotemporal deep learning model for network intrusion detection[J]. Frontiers of Information Technology & Electronic Engineering, 2025, 26(7): 1115-1130.
@article{title="CRGT-SA: an interlaced and spatiotemporal deep learning model for network intrusion detection",
author="Jue CHEN, Wanxiao LIU, Xihe QIU, Wenjing LV, Yujie XIONG",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="26",
number="7",
pages="1115-1130",
year="2025",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2400459"
}
%0 Journal Article
%T CRGT-SA: an interlaced and spatiotemporal deep learning model for network intrusion detection
%A Jue CHEN
%A Wanxiao LIU
%A Xihe QIU
%A Wenjing LV
%A Yujie XIONG
%J Frontiers of Information Technology & Electronic Engineering
%V 26
%N 7
%P 1115-1130
%@ 2095-9184
%D 2025
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.2400459
TY - JOUR
T1 - CRGT-SA: an interlaced and spatiotemporal deep learning model for network intrusion detection
A1 - Jue CHEN
A1 - Wanxiao LIU
A1 - Xihe QIU
A1 - Wenjing LV
A1 - Yujie XIONG
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 26
IS - 7
SP - 1115
EP - 1130
%@ 2095-9184
Y1 - 2025
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.2400459
Abstract: To address the challenge of cyberattacks, intrusion detection systems (IDSs) are introduced to recognize intrusions and protect computer networks. Among all these IDSs, conventional machine learning methods rely on shallow learning and have unsatisfactory performance. Unlike machine learning methods, deep learning methods are the mainstream methods because of their capability to handle mass data without prior knowledge of specific domain expertise. Concerning deep learning, long short-term memory (LSTM) and temporal convolutional networks (TCNs) can be used to extract temporal features from different angles, while convolutional neural networks (CNNs) are valuable for learning spatial properties. Based on the above, this paper proposes a novel interlaced and spatiotemporal deep learning model called CRGT-SA, which combines CNN with gated TCN and recurrent neural network (RNN) modules to learn spatiotemporal properties, and imports the self-attention mechanism to select significant features. More specifically, our proposed model splits the feature extraction into multiple steps with a gradually increasing granularity, and executes each step with a combined CNN, LSTM, and gated TCN module. Our proposed CRGT-SA model is validated using the UNSW-NB15 dataset and is compared with other compelling techniques, including traditional machine learning and deep learning models as well as state-of-the-art deep learning models. According to the simulation results, our proposed model exhibits the highest accuracy and F1-score among all the compared methods. More specifically, our proposed model achieves 91.5% and 90.5% accuracy for binary and multi-class classifications respectively, and demonstrates its ability to protect the Internet from complicated cyberattacks. Moreover, we conduct another series of simulations on the NSL-KDD dataset; the simulation results of comparison with other models further prove the generalization ability of our proposed model.
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