A Glimpse of the Whole: Path Optimization Prototypical Network for Few-Shot Encrypted Traffic Classification 来自 Semantic Scholar 喜欢 0 阅读量: 8 作者:BatchNorm,relu,Maxpool 摘要: With the prosperous application of encryption technology in network traffic, monitoring and analyzing network traffic ...
Herein, we propose a self-supervised approach for few-shot learning-based traffic sign classification. A center-awareness similarity network is designed for the traffic sign problem and trained using an optical flow dataset. Unlike existing supervised traffic sign classification ...
We reported the classification accuracy as performance metric in the closed-world scenario. We help the previous two deep learning methods (Resnet-34 and Var-CNN [16]) with HDA, compared with the pretrained few-shot method (TF [17]) and hand-crafted feature-based methods (k-NN [11], k...
A Glimpse of the Whole: Path Optimization Prototypical Network for Few-Shot Encrypted Traffic Classification 来自 Springer 喜欢 0 阅读量: 61 作者:W Li,XY Zhang,H Shi,F Liu,Z Li 摘要: With the prosperous application of encryption technology in network traffic, monitoring and analyzing network ...
Malicious traffic classificationFew-shotLightweight modelGeneralizationClassifying malicious traffic, which can trace the lineage of attackers' malicious families, is fundamental to safeguarding cybersecurity. However, the deep learning approaches currently employed require substantial volumes of data, conflicting...
Integrative few-shot learning for classification and segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18–24 June 2022; pp. 9979–9990. [Google Scholar] He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning...
We train and evaluate the model using a traffic few-shot dataset. The results show that the model outperforms the comparison model in terms of accuracy, recall and F1 value. Ablation experiments are used to verify the effect of training sample size and word vector embedding dimension on the ...