Internet of Things (IoT) can provide the interconnection and data sharing among devices, vehicles, buildings via various sensors with the development of 5G, and it has been widely used in different services such as e-commerce, heath-care, smart buildings. In the meantime, various cyber-attacks for IoT have increased and caused huge losses. Lots of security mechanisms are rapidly being proposed to prevent the potentially malicious attackers for IoT, in which machine learning especially deep learning (DL) as increasingly popular solution for security has been implemented in intrusion detection system (IDS) and others. However, the lack of enough datasets prevents the application of IDS in 5G IoT system. As one of fundamental components of IDS, network traffic classification shows a discretization, individualization and fine-grained trend which derives the different personalized classification methods for different requirements and scenarios. In this case, the data-driven DL faces the following challenges. First, there are only a few labeled datasets in the various personalized application scenarios, which undoubtedly limits the deployment of DL classification. Second, not all scenarios have rich computing capability for that training a neural network requires lots of computing resources. Therefore, this paper proposes a traffic classification method based on deep transfer learning for 5G IoT scenarios with scarce labeled data and limited computing capability, and trains the classification model by weight transferring and neural network fine-tuning. Different from the previous work that extract artificially designed features, the proposed method retains the end-to-end learning performance of DL and reduces the risk of suffering concept drift to reduce human intervention. Experimental results show that when only 10% of dataset are used to label the data samples, the classification accuracy is close to the results of full training dataset.

随着5G的发展，物联网（IoT）可以通过各种传感器提供设备、车辆、建筑物之间的互连和数据共享，已广泛应用于电子商务、医疗保健、智能建筑等不同服务领域。与此同时，针对物联网的各种网络攻击也随之增多，造成了巨大损失。许多安全机制正在迅速被提出来防止物联网的潜在恶意攻击者，其中机器学习，尤其是深度学习 (DL) 作为越来越流行的安全解决方案已在入侵检测系统 (IDS) 和其他系统中实现。然而，缺乏足够的数据集阻碍了 IDS 在 5G 物联网系统中的应用。作为 IDS 的基本组成部分之一，网络流量分类表现出离散化，个性化和细粒度化趋势，衍生出针对不同需求和场景的不同个性化分类方法。在这种情况下，数据驱动的 DL 面临以下挑战。首先，在各种个性化应用场景中，标记数据集很少，这无疑限制了DL分类的部署。其次，并非所有场景都具有丰富的计算能力，因为训练神经网络需要大量的计算资源。因此，本文针对标注数据稀少、计算能力有限的5G物联网场景，提出一种基于深度迁移学习的流量分类方法，通过权重迁移和神经网络微调训练分类模型。与以往提取人工设计特征的工作不同，概念 漂移以减少人为干预。实验结果表明，当仅使用 10% 的数据集标记数据样本时，分类准确率接近完整训练数据集的结果。