Kunsan Zhang
Kunsan Zhang has a total of 10 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
3 shared publications - Jiachun Zheng
School of Ocean Information Engineering, Jimei University, Xiamen 361000, China
3 shared publications - Renguang Zheng
Zhangzhou Power Supply Company, State Grid Fujian Electric Power Co., Ltd., No. 13 Shengli East Road, Xiangcheng District, Zhangzhou 363000, China
2 shared publications - Bingjie Xiang
School of Ocean Informattion Engineering, Jimei University, Xiamen 361000, China
1 shared publication - Jiawen Yang
School of Ocean Information Engineering, Jimei University, Xiamen 361021, China
1 shared publication - Jiawen Yang
School of Ocean Information Engineering, Jimei University, Xiamen 361000, China
1 shared publication
Latest Publications
- Open Access
IoT Network Security Threat Detection Algorithm Integrating Symmetric Routing and a Sparse Mixture-of-Experts Model
With the rapid deployment of the Internet of Things (IoT) in critical domains such as power and industrial systems, the number of IoT devices has surged, accompanied by increasingly severe network security risks. IoT networks face diverse threats, including distributed denial-of-service attacks, advanced persistent threats, and data theft or tampering, while traditional detection and defense, lacking deep feature analysis, struggle with complex and unknown attacks, degrading security threat event detection. To this end, this paper proposes an IoT network security threat detection algorithm that integrates symmetric linear routing with a sparse mixture-of-experts model. The algorithm consists of a ConvNeXt feature extractor and a sparse BiLSTM expert layer, with symmetric linear routing embedded in the gating module. ConvNeXt provides refined global and local representations, Top-K gated BiLSTM experts for the module sequence-level dependencies among ordered features, and symmetric linear routing suppresses routing bias, enabling efficient and robust detection of IoT security threats. Experimental results on the CIC-IDS2018, TON-IoT, and BoT-IoT datasets indicate that the proposed IoT network security threat detection algorithm achieves accuracies of 94.08%, %, and 99.78%, respectively. Comparative experiments show the proposed algorithm outperforms baseline and state-of-the-art models, while the ablation and Top-K studies confirm module effectiveness for IoT intrusion detection.Open AccessIoT Network Security Threat Detection Algorithm Integrating Symmetric Routing and a Sparse Mixture-of-Experts Model
With the rapid deployment of the Internet of Things (IoT) in critical domains such as power and industrial systems, the number of IoT devices has surged, accompanied by increasingly severe network security risks. IoT networks face diverse threats, including distributed denial-of-service attacks, advanced persistent threats, and data theft or tampering, while traditional detection and defense, lacking deep feature analysis, struggle with complex and unknown attacks, degrading security threat event detection. To this end, this paper proposes an IoT network security threat detection algorithm that integrates symmetric linear routing with a sparse mixture-of-experts model. The algorithm consists of a ConvNeXt feature extractor and a sparse BiLSTM expert layer, with symmetric linear routing embedded in the gating module. ConvNeXt provides refined global and local representations, Top-K gated BiLSTM experts for the module sequence-level dependencies among ordered features, and symmetric linear routing suppresses routing bias, enabling efficient and robust detection of IoT security threats. Experimental results on the CIC-IDS2018, TON-IoT, and BoT-IoT datasets indicate that the proposed IoT network security threat detection algorithm achieves accuracies of 94.08%, %, and 99.78%, respectively. Comparative experiments show the proposed algorithm outperforms baseline and state-of-the-art models, while the ablation and Top-K studies confirm module effectiveness for IoT intrusion detection. - Open Access
FFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT Security
Resource-constrained Internet of Things (IoT) devices demand efficient and robust intrusion detection systems (IDSs) to counter evolving cyber threats. The traditional IDS models, however, struggle with high computational complexity and inadequate feature extraction, limiting their accuracy and generalizability in IoT environments. To address this, we propose FFT-RDNet, a lightweight IDS framework leveraging depthwise separable convolution and frequency-domain feature fusion. An ADASYN-Tomek Links hybrid strategy first addresses class imbalances. The core innovation of FFT-RDNet lies in its novel two-dimensional spatial feature modeling approach, realized through a dedicated dual-path feature embedding module. One branch extracts discriminative statistical features in the time domain, while the other branch transforms the data into the frequency domain via Fast Fourier Transform (FFT) to capture the essential energy distribution characteristics. These time–frequency domain features are fused to construct a two-dimensional feature space, which is then processed by a streamlined residual network using depthwise separable convolution. This network effectively captures complex periodic attack patterns with minimal computational overhead. Comprehensive evaluation on the NSL-KDD and CIC-IDS2018 datasets shows that FFT-RDNet outperforms state-of-the-art neural network IDSs across accuracy, precision, recall, and F1 score (improvements: 0.22–1%). Crucially, it achieves superior accuracy with a significantly reduced computational complexity, demonstrating high efficiency for resource-constrained IoT security deployments.
Open AccessFFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT Security
Resource-constrained Internet of Things (IoT) devices demand efficient and robust intrusion detection systems (IDSs) to counter evolving cyber threats. The traditional IDS models, however, struggle with high computational complexity and inadequate feature extraction, limiting their accuracy and generalizability in IoT environments. To address this, we propose FFT-RDNet, a lightweight IDS framework leveraging depthwise separable convolution and frequency-domain feature fusion. An ADASYN-Tomek Links hybrid strategy first addresses class imbalances. The core innovation of FFT-RDNet lies in its novel two-dimensional spatial feature modeling approach, realized through a dedicated dual-path feature embedding module. One branch extracts discriminative statistical features in the time domain, while the other branch transforms the data into the frequency domain via Fast Fourier Transform (FFT) to capture the essential energy distribution characteristics. These time–frequency domain features are fused to construct a two-dimensional feature space, which is then processed by a streamlined residual network using depthwise separable convolution. This network effectively captures complex periodic attack patterns with minimal computational overhead. Comprehensive evaluation on the NSL-KDD and CIC-IDS2018 datasets shows that FFT-RDNet outperforms state-of-the-art neural network IDSs across accuracy, precision, recall, and F1 score (improvements: 0.22–1%). Crucially, it achieves superior accuracy with a significantly reduced computational complexity, demonstrating high efficiency for resource-constrained IoT security deployments.
- 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.