Abnormal Traffic Detection System Based on Feature Fusion and Sparse Transformer

Autor:	Xinjian Zhao, Weiwei Miao, Guoquan Yuan, Yu Jiang, Song Zhang, Qianmu Li
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	anomaly detection feature fusion convolutional neural network sparse transformer deep encoder Mathematics QA1-939
Zdroj:	Mathematics, Vol 12, Iss 11, p 1643 (2024)
Druh dokumentu:	article
ISSN:	12111643 2227-7390
DOI:	10.3390/math12111643
Popis:	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%.
Databáze:	Directory of Open Access Journals
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