ORIGINAL RESEARCH article
Front. Phys.
Sec. Social Physics
Volume 13 - 2025 | doi: 10.3389/fphy.2025.1592975
An Abnormal Traffic Detection Method for Chain Information Management System Network Based on Convolutional Neural Network
Provisionally accepted
- Jiangsu Vocational College of Electronics and Information, Huaian, China
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Chain information management system is widely used, providing convenience for the operation and management of enterprises. However, the problem of abnormal network traffic becomes increasingly prominent currently. Therefore, this paper proposes a convolutional neural network based on attention mechanism and autoencoder improvement, namely CBAM-AE-CRF. CBAM-AE-CRF integrates the convolutional block attention module (CBAM) into convolutional neural network to enhance the model's ability to learn anomalous features in network traffic. CBAM improves the detection accuracy of abnormal traffic in chain information management system by adaptively adjusting channel attention. At the same time, the Autoencoder module (AE) is also introduced into the model to automatically extract and reconstruct anomalous features from complex network traffic data. Finally, the conditional random field (CRF) determines the optimal label sequence based on the conditional probability distribution and applies the Viterbi algorithm to complete the sequence labeling of network traffic in chain information management system. Through extensive experimental verification, CBAM-AE-CRF can comprehensively understand the semantics of network traffic, accurately identify anomalies in network traffic of chain information management system, provide strong support for network traffic management.
Keywords: anomaly detection, Convolutional Neural Network, chain information management system, Network traffic, accuracy
Received: 13 Mar 2025; Accepted: 14 Apr 2025.
Copyright: © 2025 Liu, Liu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Chunxiang Liu, Jiangsu Vocational College of Electronics and Information, Huaian, China
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