Traffic classification is a technology for classifying and identifying sensitive information from cluttered traffic. With the increasing use of encryption and other evasion technologies, traditional content- based network traffic classification becomes impossible, and traffic classification is increasingly related to security and privacy. Many studies have been conducted to investigate traffic classification in various scenarios. A major challenge to existing schemes is extending traffic classification technology to a broader space. In other words, most traffic classification work is not universal and can only show great performance on specific datasets. In this article, we present a systematic approach to optimizing feature selection for encrypted traffic classification. We summarize the optional encrypted traffic features and analyze the approaches of feature selection in detail for different datasets. The experimental result demonstrates that our scheme is more accurate and universal than other state-of-the-art approaches. More precisely, our mechanism provides a guideline for future research in the field of traffic classification.

中文翻译：

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优化特征选择以实现高效的加密流量分类：系统方法


流量分类是一种从杂乱的流量中分类识别敏感信息的技术。随着加密和其他规避技术的越来越多地使用，传统的基于内容的网络流量分类变得不可能，并且流量分类越来越与安全和隐私相关。已经进行了许多研究来调查各种场景下的流量分类。现有方案的一个主要挑战是将流量分类技术扩展到更广阔的空间。换句话说，大多数流量分类工作并不通用，只能在特定数据集上表现出出色的性能。在本文中，我们提出了一种优化加密流量分类特征选择的系统方法。我们总结了可选的加密流量特征，并详细分析了不同数据集的特征选择方法。实验结果表明，我们的方案比其他最先进的方法更准确和通用。更准确地说，我们的机制为流量分类领域的未来研究提供了指导。