Abnormal Traffic Detection System Based on Feature Fusion and Sparse Transformer

Xinjian Zhao, Weiwei Miao, Guoquan Yuan, Yu Jiang (), Song Zhang and Qianmu Li
Additional contact information
Xinjian Zhao: State Grid Jiangsu Electric Power Co., Ltd., Information & Telecommunication Branch, Nanjing 210024, China
Weiwei Miao: State Grid Jiangsu Electric Power Co., Ltd., Information & Telecommunication Branch, Nanjing 210024, China
Guoquan Yuan: State Grid Jiangsu Electric Power Co., Ltd., Information & Telecommunication Branch, Nanjing 210024, China
Yu Jiang: School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Song Zhang: State Grid Jiangsu Electric Power Co., Ltd., Information & Telecommunication Branch, Nanjing 210024, China
Qianmu Li: School of Cyber Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Mathematics, 2024, vol. 12, issue 11, 1-14

Abstract: This paper presents a feature fusion and sparse transformer-based anomalous traffic detection system (FSTDS). FSTDS utilizes a feature fusion network to encode the traffic data sequences and extracting features, fusing them into coding vectors through shallow and deep convolutional networks, followed by deep coding using a sparse transformer to capture the complex relationships between network flows; finally, a multilayer perceptron is used to classify the traffic and achieve anomaly traffic detection. The feature fusion network of FSTDS improves feature extraction from small sample data, the deep encoder enhances the understanding of complex traffic patterns, and the sparse transformer reduces the computational and storage overhead and improves the scalability of the model. Experiments demonstrate that the number of FSTDS parameters is reduced by up to nearly half compared to the baseline, and the success rate of anomalous flow detection is close to 100%.

Keywords: anomaly detection; feature fusion; convolutional neural network; sparse transformer; deep encoder (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/11/1643/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/11/1643/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:11:p:1643-:d:1400804

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().