Abnormal Traffic Detection Method Based on Traffic Spatial-temporal Features and Adaptive Weighting Coefficients

GU Wei; XING Hongyan; HOU Tianhao

doi:10.11999/JEIT230825

Volume 46 Issue 6

Jun. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(6): 2647-2654

GU Wei, XING Hongyan, HOU Tianhao. Abnormal Traffic Detection Method Based on Traffic Spatial-temporal Features and Adaptive Weighting Coefficients[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2647-2654. doi: 10.11999/JEIT230825

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GU Wei, XING Hongyan, HOU Tianhao. Abnormal Traffic Detection Method Based on Traffic Spatial-temporal Features and Adaptive Weighting Coefficients[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2647-2654. doi: 10.11999/JEIT230825

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Abnormal Traffic Detection Method Based on Traffic Spatial-temporal Features and Adaptive Weighting Coefficients

doi: 10.11999/JEIT230825 cstr: 32379.14.JEIT230825

School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Funds: The National Natural Science Foundation of China (62171228), The National Key R&D Program of China (2021YFE0105500)

Received Date: 2023-08-01
Rev Recd Date: 2024-01-15

Available Online: 2024-01-19

Publish Date: 2024-06-30

Abstract

Abstract

Considering the problem that the performance of the traditional abnormal traffic detection models is limited by the low utilization of spatiotemporal features of traffic data, an abnormal traffic detection method MSECNN-BiLSTM based on the combination of Convolutional Neural Network (CNN), Multi head Squeeze Excitation mechanism (MSE), and Bidirectional Long Short-Term Memory (BiLSTM) network is proposed. The one-dimensional CNN is used to capture abnormal traffic features at spatial scales. The MSE mechanism is introduced to adaptively calibrate the feature weights and strengthen the model’s ability to correlate global features from multiple perspectives. The traffic features are input into BiLSTM to capture the temporal dependencies of the traffic data and further model the relationship of network traffic on the time scale. The softmax classifier is employed for traffic detection. The experimental results verify that the proposed model is effective in the field of abnormal traffic detection.
- Abnormal traffic detection,
- Convolutional Neural Network(CNN),
- Long Short-Term Memory(LSTM) network,
- Squeeze excitation mechanism

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References(21)

References

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