论文标题
6GCVAE：IPv6目标生成的封闭卷积卷积变量自动编码器
6GCVAE: Gated Convolutional Variational Autoencoder for IPv6 Target Generation
论文作者
论文摘要
对于网络测量领域的研究人员来说，IPv6扫描一直是一个挑战。由于具有相当大的IPv6地址空间，虽然最近的网络速度和计算能力得到了提高，但使用蛮力方法探测IPv6的整个网络空间几乎是不可能的。系统需要采用算法方法来生成更多可能的活跃目标候选集进行探测。在本文中，我们首先尝试使用深度学习来设计此类IPv6目标生成算法。该模型通过堆叠封闭式卷积层以构建变异自动编码器（VAE）来有效地学习地址结构。我们还介绍了两种地址分类方法，以改善目标生成的模型效应。实验表明，我们的方法6GCVAE在两个活动地址数据集中超过了常规VAE模型和最新目标生成算法。
IPv6 scanning has always been a challenge for researchers in the field of network measurement. Due to the considerable IPv6 address space, while recent network speed and computational power have been improved, using a brute-force approach to probe the entire network space of IPv6 is almost impossible. Systems are required an algorithmic approach to generate more possible active target candidate sets to probe. In this paper, we first try to use deep learning to design such IPv6 target generation algorithms. The model effectively learns the address structure by stacking the gated convolutional layer to construct Variational Autoencoder (VAE). We also introduce two address classification methods to improve the model effect of the target generation. Experiments indicate that our approach 6GCVAE outperformed the conventional VAE models and the state-of-the-art target generation algorithm in two active address datasets.
