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

Αποθηκεύτηκε σε:

Λεπτομέρειες βιβλιογραφικής εγγραφής
Εκδόθηκε σε:	Computers vol. 14, no. 7 (2025), p. 248-265
Κύριος συγγραφέας:	Li, Junwei
Άλλοι συγγραφείς:	Pan Zhisong
Έκδοση:	MDPI AG
Θέματα:	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
Διαθέσιμο Online:	Citation/Abstract Full Text + Graphics Full Text - PDF
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Περιγραφή
Περίληψη:	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
Πηγή:	Advanced Technologies & Aerospace Database