App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 8

Network intrusion detection method based on multi-scale spatiotemporal feature fusion

Xu Li1
Su Na1
Pei Houqing1
Wang Jingjun1
Ji Shujuan2
1. College of Intelligent Equipment, Shandong University of Science and Technology, Tai'an Shandong 271000, China
2. School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao Shandong 266555, China
DOI: 10.19734/j.issn.1001-3695.2025.11.0493

Abstract

To address the performance degradation of existing network intrusion detection methods caused by insufficient feature representation capability, imbalanced data distribution, and the neglect of importance differences between traffic features and attack categories, a detection method based on feature selection and multi-scale spatiotemporal fusion is developed. First, a class-specific and conditional mutual information fusion (CCMIF) strategy is employed to select traffic features with strong correlation and low redundancy. Then, a multi-scale spatiotemporal fusion feature extraction module (MSTF-FE) is constructed to jointly capture long-term temporal dependencies and multi-scale spatial characteristics of network traffic. In addition, supervised contrastive learning is introduced to enhance the discriminative ability of deep feature representations, and a class-sample quantity adaptive temperature mechanism (CSQAT) is designed to optimize the loss function, thereby alleviating the unclear inter-class boundaries in the feature space under imbalanced data conditions. Finally, a multilayer perceptron is adopted for classification. Experimental results on the UNSW-NB15 and NSL-KDD datasets demonstrate that the proposed method achieves detection accuracies of 78.48% and 84.61%, respectively, indicating its effectiveness and applicability in complex network environments.

Foundation Support

国家自然科学基金资助项目(72572095)
山东省自然科学基金资助项目(ZR2023MF070)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2025.11.0493
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 8

Publish History

[2026-04-23] Accepted Paper

Cite This Article

徐力, 苏娜, 裴厚清, 等. 基于多尺度时空特征融合的网络入侵检测方法 [J]. 计算机应用研究, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0493. (Xu Li, Su Na, Pei Houqing, et al. Network intrusion detection method based on multi-scale spatiotemporal feature fusion [J]. Application Research of Computers, 2026, 43 (8). (2026-04-30). https://doi.org/10.19734/j.issn.1001-3695.2025.11.0493. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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