Renguang Zheng
Research Subjects
Renguang Zheng has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Chaopeng Li
School of Ocean Information Engineering, Jimei University, Xiamen 361000, China
1 shared publication - Jiachun Zheng
School of Ocean Information Engineering, Jimei University, Xiamen 361000, China
1 shared publication - Jiawen Yang
School of Ocean Information Engineering, Jimei University, Xiamen 361021, China
1 shared publication - Kunsan Zhang
Zhangzhou Power Supply Company, State Grid Fujian Electric Power Co., Ltd., No. 13 Shengli East Road, Xiangcheng District, Zhangzhou 363000, China
1 shared publication - Shidan Sun
School of Ocean Information Engineering, Jimei University, Xiamen 361021, China
1 shared publication - Song Zhang
State Grid Fujian Electric Power Co., Ltd. Zhangzhou Power Supply Company, No. 13 Shengli East Road, Xiangcheng District, Zhangzhou 363000, China
1 shared publication
Latest Publications
- Open Access
SE-DWNet: An Advanced ResNet-Based Model for Intrusion Detection with Symmetric Data Distribution
With the rapid increase in cyber-attacks, intrusion detection systems (IDS) have become essential for network security. However, traditional IDS methods often struggle with class imbalance, leading to asymmetric data distributions that adversely affect detection performance and model generalization. To address this issue and enhance detection accuracy, this paper proposes SE-DWNet, a residual network model incorporating an attention mechanism and one-dimensional depthwise separable convolution, trained on a symmetrically preprocessed dataset using SMOTETomek sampling. First, the feature distributions of the training and test datasets are analyzed using box plots, highlighting the impact of feature difference. To mitigate this difference and restore a more symmetric data distribution, we employ the SMOTETomek integrated sampling method in conjunction with a Focal Loss function. Subsequently, a lightweight residual network, incorporating the SE module and the Res-DWNet module, is designed to improve detection accuracy while maintaining computational efficiency. Extensive experiments on the NSL-KDD, CICIDS2018, and ToN-IoT datasets demonstrate that SE-DWNet outperforms existing neural network-based IDS models, achieving accuracy, precision, recall, and F1-score improvements ranging from 0.17% to 5.33%. The results confirm the effectiveness and superiority of the proposed approach in intrusion detection tasks.
Open AccessSE-DWNet: An Advanced ResNet-Based Model for Intrusion Detection with Symmetric Data Distribution
With the rapid increase in cyber-attacks, intrusion detection systems (IDS) have become essential for network security. However, traditional IDS methods often struggle with class imbalance, leading to asymmetric data distributions that adversely affect detection performance and model generalization. To address this issue and enhance detection accuracy, this paper proposes SE-DWNet, a residual network model incorporating an attention mechanism and one-dimensional depthwise separable convolution, trained on a symmetrically preprocessed dataset using SMOTETomek sampling. First, the feature distributions of the training and test datasets are analyzed using box plots, highlighting the impact of feature difference. To mitigate this difference and restore a more symmetric data distribution, we employ the SMOTETomek integrated sampling method in conjunction with a Focal Loss function. Subsequently, a lightweight residual network, incorporating the SE module and the Res-DWNet module, is designed to improve detection accuracy while maintaining computational efficiency. Extensive experiments on the NSL-KDD, CICIDS2018, and ToN-IoT datasets demonstrate that SE-DWNet outperforms existing neural network-based IDS models, achieving accuracy, precision, recall, and F1-score improvements ranging from 0.17% to 5.33%. The results confirm the effectiveness and superiority of the proposed approach in intrusion detection tasks.