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 gene...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/1424-8220/25/15/4584 |
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| author | Bingjie Xiang Renguang Zheng Kunsan Zhang Chaopeng Li Jiachun Zheng |
| author_facet | Bingjie Xiang Renguang Zheng Kunsan Zhang Chaopeng Li Jiachun Zheng |
| author_sort | Bingjie Xiang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fb987efae13f4c668589ae622e7227cd |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-fb987efae13f4c668589ae622e7227cd2025-08-20T03:36:22ZengMDPI AGSensors1424-82202025-07-012515458410.3390/s25154584FFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT SecurityBingjie Xiang0Renguang Zheng1Kunsan Zhang2Chaopeng Li3Jiachun Zheng4School of Ocean Informattion Engineering, Jimei University, Xiamen 361000, ChinaState Grid Fujian Electric Power Co., Ltd., Zhangzhou Power Supply Company, No. 13 Shengli East Road, Xiangcheng District, Zhangzhou 363000, ChinaState Grid Fujian Electric Power Co., Ltd., Zhangzhou Power Supply Company, No. 13 Shengli East Road, Xiangcheng District, Zhangzhou 363000, ChinaSchool of Ocean Informattion Engineering, Jimei University, Xiamen 361000, ChinaSchool of Ocean Informattion Engineering, Jimei University, Xiamen 361000, ChinaResource-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.https://www.mdpi.com/1424-8220/25/15/4584internet of thingsFFTdepthwise separable convolutionresidual networkhybrid sampling |
| spellingShingle | Bingjie Xiang Renguang Zheng Kunsan Zhang Chaopeng Li Jiachun Zheng FFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT Security Sensors internet of things FFT depthwise separable convolution residual network hybrid sampling |
| title | FFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT Security |
| title_full | FFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT Security |
| title_fullStr | FFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT Security |
| title_full_unstemmed | FFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT Security |
| title_short | FFT-RDNet: A Time–Frequency-Domain-Based Intrusion Detection Model for IoT Security |
| title_sort | fft rdnet a time frequency domain based intrusion detection model for iot security |
| topic | internet of things FFT depthwise separable convolution residual network hybrid sampling |
| url | https://www.mdpi.com/1424-8220/25/15/4584 |
| work_keys_str_mv | AT bingjiexiang fftrdnetatimefrequencydomainbasedintrusiondetectionmodelforiotsecurity AT renguangzheng fftrdnetatimefrequencydomainbasedintrusiondetectionmodelforiotsecurity AT kunsanzhang fftrdnetatimefrequencydomainbasedintrusiondetectionmodelforiotsecurity AT chaopengli fftrdnetatimefrequencydomainbasedintrusiondetectionmodelforiotsecurity AT jiachunzheng fftrdnetatimefrequencydomainbasedintrusiondetectionmodelforiotsecurity |