Multilevel Identification and Classification Analysis of Tor on Mobile and PC Platforms
Multilevel Identification and Classification Analysis of Tor on Mobile and PC Platforms
复制标题
移动端和PC端Tor的多级识别和分类分析
DOI：
10.1109/tii.2020.2988870
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发表时间：
2021-02
影响因子：
12.3
通讯作者：
Victor S. Sheng
中科院分区：
文献类型：
--
作者：
Liangmin Wang;Hantao Mei;Victor S. Sheng
在数字化和自动化系统中，越来越多的智能设备已成为工业物联网的重要组成部分。然而，工业物联网缺乏安全性使人们面临着来自暗网的前所未有的威胁。流量分类是防范匿名攻击的重要手段。然而，智能手机在日常生活中的日益普及正在深刻地改变着网络流量的性质，这使得流量分类更具挑战性。本文提出了一种基于网络流特征的Tor流量识别和多级分类框架，分别在移动平台和PC平台上实现了匿名流量（L1）、匿名流量类型（L2）和应用（L3）的识别。我们进一步分析了移动平台和PC平台之间的差异。在移动平台上，时间相关特征的影响大于非时间相关特征的影响，而在PC平台上则相反。在移动平台上识别和分类Tor类型（L2）和特定Tor应用（L3）比在PC平台上更难，包括使用不同数量的特征和早期识别和分类。
In digitalized and automated systems, more and more intelligent devices have become an import part of industrial Internet of Things (IIOT). However, the lack of security in IIOT makes people facing unprecedented threats from the Dark web. Traffic classification is an important means to prevent anonymous attacks. However, the growing usage of smartphones in daily life is deeply changing the nature of network traffic, which makes traffic classification more challenging. In this article, we propose a Tor traffic identification and multilevel classification framework based on network flow features, which realizes the identification of anonymous traffic (L1), traffic types (L2) of anonymous traffic, and applications (L3) on a mobile and a PC platform, respectively. We further analyze differences between the mobile and the PC platform. We conclude that the impact of time-related features is higher than that of the nontime-related features on the mobile platform, while it is opposite on the PC platform. And it is more difficult to identify and classify Tor types (L2) and specific Tor applications (L3) on the mobile platform than on the PC platform, including using different number of features and early identification and classification.
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DOI：
10.3182/20110828-6-it-1002.03712
发表时间：
2014-07
影响因子：
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DOI：
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1992-10
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2011-08
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