The Internet of Things (IoT) has witnessed unprecedented growth, resulting in a massive influx of diverse network traffic from interconnected devices. Effectively classifying this network traffic is crucial for optimizing resource allocation, enhancing security measures, and ensuring efficient network management in IoT systems. Deep learning has emerged as a powerful technique for network traffic classification due to its ability to automatically learn complex patterns and representations from raw data. This survey paper aims to provide a comprehensive overview of the existing deep learning approaches employed in network traffic classification specifically tailored for IoT environments. By systematically analyzing and categorizing the latest research contributions in this domain, we explore the strengths and limitations of various deep learning models in handling the unique challenges posed by IoT network traffic. Through this survey, we aim to offer researchers and practitioners valuable insights, identify research gaps, and provide directions for future research to further enhance the effectiveness and efficiency of deep learning-based network traffic classification in IoT.

翻译：物联网（IoT）经历了前所未有的增长，导致互联设备产生的多样化网络流量大规模激增。有效分类这些网络流量对于优化资源分配、增强安全措施以及确保物联网系统中高效的网络管理至关重要。深度学习因其能够从原始数据中自动学习复杂模式和表示，已成为网络流量分类的强大技术。本综述论文旨在全面概述专门针对物联网环境所采用的现有深度学习方法。通过系统分析和分类该领域的最新研究贡献，我们探讨了各种深度学习模型在处理物联网网络流量独特挑战时的优势与局限性。通过本次综述，我们旨在为研究人员和实践者提供宝贵见解，识别研究空白，并为未来研究指明方向，以进一步提升基于深度学习的物联网网络流量分类的有效性和效率。