Research on Network Transmission Anomaly Detection and Optimization Based on Signal Decomposition and LSTM

Ning Pan

doi:10.47297/taposatWSP2633-456923.20250608

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Research on Network Transmission Anomaly Detection and Optimization Based on Signal Decomposition and LSTM

Volume 6, Issue 8

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Research on Network Transmission Anomaly Detection and Optimization Based on Signal Decomposition and LSTM
Research on Network Transmission Anomaly Detection and Optimization Based on Signal Decomposition and LSTM
Big Data Center, Hubei University, Wuhan, Hubei, 430062, China
Vol. 6, Issue 8, Pages: 124-128(2025)
Published：30 August 2025
DOI：10.47297/taposatWSP2633-456923.20250608
Accepted：

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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：

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.

摘要

Abstract

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

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.

关键词

Keywords

references

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