Traffic classification is a fundamental tool for network management, measurement and security. As the new services with diversified QoS requirements are evolving, traffic classification plays a more significant role in ensuring end-to-end performance guarantees. Several efforts have introduced deep learning (DL) to train traffic classifiers without manual features, however, these classifiers depend heavily on numerous samples and their quality. Indeed, it is unrealistic to obtain sufficient and representative samples in the underlying real network. In this paper, we propose a highly accurate traffic classification model by an Ensemble Learning framework, where Convolutional Recurrent Neural Networks are integrated into the Bagging to train the classifier using only small-scale datasets. Specifically, ensemble learning employs a combinatorial design of three base models to build more accurate and robust learning models so that absorbing features opens up the possibility of training classifiers on small sample sets. We conduct comprehensive experiments with a real-world dataset encompassing 20 applications. Extensive experiment results demonstrate that even with a mere 10% of training samples, our proposed model attains a classification accuracy of 93.94% and a classification precision of 94.33%, outperforming multiple other cutting-edge methods.

Abstract Unsupervised domain adaptation (UDA) is increasingly used for 3D point cloud semantic segmentation tasks due to its ability to address the issue of missing labels for new domains. However, most existing unsupervised domain adaptation methods focus only on uni‐modal data and are rarely applied to multi‐modal data. Therefore, we propose a cross‐modal UDA on multi‐modal datasets that contain 3D point clouds and 2D images for 3D Semantic Segmentation. Specifically, we first propose a Dual discriminator‐based Domain Adaptation (Dd‐bDA) module to enhance the adaptability of different domains. Second, given that the robustness of depth information to domain shifts can provide more details for semantic segmentation, we further employ a Dense depth Feature Fusion (DdFF) module to extract image features with rich depth cues. We evaluate our model in four unsupervised domain adaptation scenarios, i.e., dataset‐to‐dataset (A2D2 → SemanticKITTI), Day‐to‐Night, country‐to‐country (USA → Singapore), and synthetic‐to‐real (VirtualKITTI → SemanticKITTI). In all settings, the experimental results achieve significant improvements and surpass state‐of‐the‐art models.