TY  - EJOU
AU  - Wang, Xiangbin 
AU  - Yuan, Qingjun 
AU  - Niu, Weina 
AU  - Meng, Qianwei 
AU  - Wang, Yongjuan 
AU  - Gu, Chunxiang 

TI  - DMF: A Deep Multimodal Fusion-Based Network Traffic Classification Model
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - With the rise of encrypted traffic, traditional network analysis methods have become less effective, leading to a shift towards deep learning-based approaches. Among these, multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic, improving classification accuracy. However, existing research predominantly relies on late fusion techniques, which hinder the full utilization of deep features within the data. To address this limitation, we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature extraction. Specifically, our approach performs real-time fusion of modalities at each stage of feature extraction, enhancing feature representation at each level and preserving inter-level correlations for more effective learning. This continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic, while boosting its robustness and adaptability to evolving network conditions. Experimental results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%, outperforming existing multimodal learning-based methods.
KW  - Deep fusion; intrusion detection; multimodal learning; network traffic classification

DO  - 10.32604/cmc.2025.061767