6卷 第8期
Big Data Center, Hubei University, Wuhan, Hubei, 430062, China
纸质出版：2025-08-30
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(2025). Research on Network Transmission Anomaly Detection and Optimization Based on Signal Decomposition and LSTM. 科技理论与实践（英文版）, 6(8), 124-128.
Ning Pan.Research on Network Transmission Anomaly Detection and Optimization Based on Signal Decomposition and LSTM[J].Theory and Practice of Science and Technology,2025,06(08):124-128.
(2025). Research on Network Transmission Anomaly Detection and Optimization Based on Signal Decomposition and LSTM. 科技理论与实践（英文版）, 6(8), 124-128. DOI： 10.47297/taposatWSP2633-456923.20250608.
Ning Pan.Research on Network Transmission Anomaly Detection and Optimization Based on Signal Decomposition and LSTM[J].Theory and Practice of Science and Technology,2025,06(08):124-128. DOI： 10.47297/taposatWSP2633-456923.20250608.
In high-performance network transmission
traffic presents significant nonlinear and nonsmooth characteristics
which brings challenges to anomaly detection. In order to achieve accurate identification and trend warning of network anomalies
a network transmission anomaly detection and optimization method based on ensemble empirical modal decomposition and attention mechanism two-way long short-term memory network is proposed. The study adopts the ensemble empirical modal decomposition to extract multi-scale features
combines with Hurst index to locate anomalous segments
and realizes high-precision network traffic prediction and early warning through the bi-directional long- and short-term memory network with the introduction of the attention mechanism. Experimental results show that the method achieves 0.96
0.95
0.95
0.95
and 0.95 in accuracy
precision
recall
and F1
respectively; the detection latency is stabilized at about 280ms
and the occupancy rate of the central processor does not exceed 30%. The study shows that the method can improve the detection accuracy and advance warning ability of anomalous traffic in complex encrypted network environment
and provide efficient and intelligent technical support for network security protection.
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