用于物联网网络流量分类的时间分布式特征学习
Time-Distributed Feature Learning for Internet of Things Network Traffic Classification
AI总结：
针对物联网NTC忽略包内与流内时间信息的问题，提出时间分布式整体特征学习方法，可同时用于常规与CoS分类，并在真实数据集上平均提升准确率13.5%。
摘要：
基于深度学习的网络流量分类（NTC）技术（包括常规分类器和服务等级（CoS）分类器）是一种流行工具，可辅助物联网（IoT）网络的服务质量（QoS）和无线电资源管理。整体时间特征由数据包内部、数据包之间以及流之间的包间、包内和伪时间特征组成，可在不依赖问题中已定义类别的情况下提供关于网络服务的最大信息。当前解决方案中的常规时空特征仅提取数据包与流之间的空间和时间信息，而忽略了IoT流量中数据包内部和流内部的信息。因此，我们提出一种新的、高效的整体特征提取方法，用于基于深度学习的NTC，采用时间分布式特征学习以最大化NTC的准确性。我们在深度学习层上应用时间分布式包装器，以帮助提取伪时间特征和时空特征。伪时间特征在数学上很难解释，因为在深度学习中由黑箱提取它们；然而，由于时间分布式包装器，这些特征具有时间性，因此我们称其为伪时间特征。由于我们的方法能高效学习整体时间特征，因此可将其扩展到常规NTC和CoS NTC。我们的解决方案证明，伪时间特征和时空特征能够显著提升任意NTC的鲁棒性和性能。我们在不同的真实世界数据集上从理论和实验两方面分析了该解决方案。实验结果表明，整体时间的时间分布式特征学习方法的准确率平均比最先进的常规分类器和CoS分类器高13.5%。
英文摘要：
Deep learning-based network traffic classification (NTC) techniques, including conventional and class-of-service (CoS) classifiers, are a popular tool that aids in the quality of service (QoS) and radio resource management for the Internet of Things (IoT) network. Holistic temporal features consist of inter-, intra-, and pseudo-temporal features within packets, between packets, and among flows, providing the maximum information on network services without depending on defined classes in a problem. Conventional spatio-temporal features in the current solutions extract only space and time information between packets and flows, ignoring the information within packets and flow for IoT traffic. Therefore, we propose a new, efficient, holistic feature extraction method for deep-learning-based NTC using time-distributed feature learning to maximize the accuracy of the NTC. We apply a time-distributed wrapper on deep-learning layers to help extract pseudo-temporal features and spatio-temporal features. Pseudo-temporal features are mathematically complex to explain since, in deep learning, a black box extracts them. However, the features are temporal because of the time-distributed wrapper; therefore, we call them pseudo-temporal features. Since our method is efficient in learning holistic-temporal features, we can extend our method to both conventional and CoS NTC. Our solution proves that pseudo-temporal and spatial-temporal features can significantly improve the robustness and performance of any NTC. We analyze the solution theoretically and experimentally on different real-world datasets. The experimental results show that the holistic-temporal time-distributed feature learning method, on average, is 13.5% more accurate than the state-of-the-art conventional and CoS classifiers.
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