An abnormal traffic detection method for chain information management system network based on convolutional neural network
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 mecha...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Physics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphy.2025.1592975/full |
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| author | Chao Liu Chunxiang Liu Changrong Liu |
| author_facet | Chao Liu Chunxiang Liu Changrong Liu |
| author_sort | Chao Liu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5f116af8ea3341b78a5635da5e9b273a |
| institution | OA Journals |
| issn | 2296-424X |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physics |
| spelling | doaj-art-5f116af8ea3341b78a5635da5e9b273a2025-08-20T02:20:07ZengFrontiers Media S.A.Frontiers in Physics2296-424X2025-04-011310.3389/fphy.2025.15929751592975An abnormal traffic detection method for chain information management system network based on convolutional neural networkChao Liu0Chunxiang Liu1Changrong Liu2Business College, Jiangsu Vocational College of Electronics and Information, Huaian, Jiangsu, ChinaSchool of Marxism, Jiangsu Vocational College of Electronics and Information, Huaian, Jiangsu, ChinaSchool of Computer Science and Communication, Jiangsu Vocational College of Electronics and Information, Huaian, Jiangsu, ChinaChain 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.https://www.frontiersin.org/articles/10.3389/fphy.2025.1592975/fullanomaly detectionconvolutional neural networkchain information management systemnetwork trafficaccuracy |
| spellingShingle | Chao Liu Chunxiang Liu Changrong Liu An abnormal traffic detection method for chain information management system network based on convolutional neural network Frontiers in Physics anomaly detection convolutional neural network chain information management system network traffic accuracy |
| title | An abnormal traffic detection method for chain information management system network based on convolutional neural network |
| title_full | An abnormal traffic detection method for chain information management system network based on convolutional neural network |
| title_fullStr | An abnormal traffic detection method for chain information management system network based on convolutional neural network |
| title_full_unstemmed | An abnormal traffic detection method for chain information management system network based on convolutional neural network |
| title_short | An abnormal traffic detection method for chain information management system network based on convolutional neural network |
| title_sort | abnormal traffic detection method for chain information management system network based on convolutional neural network |
| topic | anomaly detection convolutional neural network chain information management system network traffic accuracy |
| url | https://www.frontiersin.org/articles/10.3389/fphy.2025.1592975/full |
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