Open Access

ARTICLE

SA-ResNet: An Intrusion Detection Method Based on Spatial Attention Mechanism and Residual Neural Network Fusion

Zengyu Cai^1,*, Yuming Dai¹, Jianwei Zhang^2,3,*, Yuan Feng⁴

1 School of Computer Science and Technology, Zhengzhou University of Light Industry, Zhengzhou, 450066, China
2 College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450066, China
3 Faculty of Information Engineering, Xuchang Vocational Technical College, Xuchang, 461000, China
4 School of Electronic Information, Zhengzhou University of Light Industry, Zhengzhou, 450066, China

* Corresponding Authors: Zengyu Cai. Email: ; Jianwei Zhang. Email:

Computers, Materials & Continua 2025, 83(2), 3335-3350. https://doi.org/10.32604/cmc.2025.061206

Received 19 November 2024; Accepted 24 February 2025; Issue published 16 April 2025

Abstract

The rapid development and widespread adoption of Internet technology have significantly increased Internet traffic, highlighting the growing importance of network security. Intrusion Detection Systems (IDS) are essential for safeguarding network integrity. To address the low accuracy of existing intrusion detection models in identifying network attacks, this paper proposes an intrusion detection method based on the fusion of Spatial Attention mechanism and Residual Neural Network (SA-ResNet). Utilizing residual connections can effectively capture local features in the data; by introducing a spatial attention mechanism, the global dependency relationships of intrusion features can be extracted, enhancing the intrusion recognition model’s focus on the global features of intrusions, and effectively improving the accuracy of intrusion recognition. The proposed model in this paper was experimentally verified on the NSL-KDD dataset. The experimental results show that the intrusion recognition accuracy of the intrusion detection method based on SA-ResNet has reached 99.86%, and its overall accuracy is 0.41% higher than that of traditional Convolutional Neural Network (CNN) models.

---

Keywords

---