Abnormal Traffic Detection System Based on Feature Fusion and Sparse Transformer

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, followe...

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Bibliographic Details
Published in:Mathematics (Basel) Vol. 12; no. 11; p. 1643
Main Authors: Zhao, Xinjian, Miao, Weiwei, Yuan, Guoquan, Jiang, Yu, Zhang, Song, Li, Qianmu
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.06.2024
Subjects:
anomaly detection
Artificial intelligence
Chatbots
Coding
Communications traffic
convolutional neural network
Data fusion
deep encoder
Deep learning
Feature extraction
feature fusion
Machine learning
Methods
Multilayer perceptrons
Neural networks
Protocol
Signal detection (Electronics)
sparse transformer
Support vector machines
Technology application
Testing
Traffic information
Transformers
China
ISSN:2227-7390, 2227-7390
Online Access:Get full text
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Summary: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%.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math12111643