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首页> 外文会议>Proceedings of the 1st Network Traffic Measurement and Analysis Conference >Large-scale classification of IPv6-IPv4 siblings with variable clock skew
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Large-scale classification of IPv6-IPv4 siblings with variable clock skew

机译：具有可变时钟偏斜的IPv6-IPv4同级的大规模分类

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摘要

Linking the growing IPv6 deployment to existing IPv4 addresses is an interesting field of research, be it for network forensics, structural analysis, or reconnaissance. In this work, we focus on classifying pairs of server IPv6 and IPv4 addresses as siblings, i.e., running on the same machine. Our methodology leverages active measurements of TCP timestamps and other network characteristics, which we measure against a diverse ground truth of 682 hosts. We define and extract a set of features, including estimation of variable (opposed to constant) remote clock skew. On these features, we train a manually crafted algorithm as well as a machine-learned decision tree. By conducting several measurement runs and training in cross-validation rounds, we aim to create models that generalize well and do not overfit our training data. We find both models to exceed 99% precision in train and test performance. We validate scalability by classifying 149k siblings in a large-scale measurement of 371k sibling candidates. We argue that this methodology, thoroughly cross-validated and likely to generalize well, can aid comparative studies of IPv6 and IPv4 behavior in the Internet. Striving for applicability and replicability, we release ready-to-use source code and raw data from our study.
机译：将不断增长的IPv6部署链接到现有IPv4地址是一个有趣的研究领域，无论是用于网络取证，结构分析还是侦察。在这项工作中，我们专注于将服务器IPv6和IPv4地址对分类为同级，即在同一台计算机上运行。我们的方法利用了TCP时间戳和其他网络特征的主动度量，这些度量是针对682台主机的多种基础事实进行度量的。我们定义并提取了一组功能，包括估计变量（与常量相对）的远程时钟偏斜。在这些功能上，我们训练了人工制作的算法以及机器学习的决策树。通过进行多次测量运行并在交叉验证轮中进行培训，我们的目标是创建能够很好地泛化且不会过度拟合我们的培训数据的模型。我们发现这两种模型的训练和测试性能都超过99％的精度。我们通过对371k个同胞候选物的大规模测量中的149k个同胞进行分类来验证可扩展性。我们认为，这种方法经过完全交叉验证并且可能很好地推广，可以帮助对Internet中IPv6和IPv4行为进行比较研究。为了实现适用性和可复制性，我们发布了研究中的现成可用的源代码和原始数据。

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