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Dark Web Traffic Classification Based on Spatial–Temporal Feature Fusion and Attention Mechanism

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Publicado en:Computers vol. 14, no. 7 (2025), p. 248-265
Autor principal: Li, Junwei
Otros Autores: Pan Zhisong
Publicado:
MDPI AG
Materias:
Accuracy
Machine learning
Packets (communication)
Search engines
Deep learning
Datasets
Classification
Information storage
Drug trafficking
Spatiotemporal data
Neural networks
Support vector machines
Traffic flow
Methods
Algorithms
Dark web
Acceso en línea:Citation/Abstract
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Resumen:There is limited research on current traffic classification methods for dark web traffic and the classification results are not very satisfactory. To improve the prediction accuracy and classification precision of dark web traffic, a classification method (CLA) based on spatial–temporal feature fusion and an attention mechanism is proposed. When processing raw bytes, the combination of a CNN and LSTM is used to extract local spatial–temporal features from raw data packets, while an attention module is introduced to process key spatial–temporal data. The experimental results show that this model can effectively extract and utilize the spatial–temporal features of traffic data and use the attention mechanism to measure the importance of different features, thereby achieving accurate predictions of different dark web traffic. In comparative experiments, the accuracy, recall rate, and F1 score of this model are higher than those of other traditional methods.
ISSN:2073-431X
DOI:10.3390/computers14070248
Fuente:Advanced Technologies & Aerospace Database