论文标题
6Veclm：IPv6目标生成的向量空间中的语言建模
6VecLM: Language Modeling in Vector Space for IPv6 Target Generation
论文作者
论文摘要
快速IPv6扫描在网络测量领域中具有挑战性，因为它需要探索整个IPv6地址空间，但受到当前计算能力的限制。研究人员建议通过算法分析活性种子集，以获取可能的活跃目标候选集合来探测。但是，IPv6解决了缺乏语义信息，并且包含许多寻址方案，从而导致设计有效算法的困难。在本文中，我们介绍了我们的方法6VECLM，以探索实现此类目标生成算法的方法。该体系结构可以将地址映射到矢量空间以解释语义关系，并使用变压器网络来构建IPv6语言模型来预测地址序列。实验表明我们的方法可以在地址空间上执行语义分类。通过添加新一代方法，我们的模型具有与传统语言模型相比具有可控的单词创新能力。该工作的表现超过了两个活动地址数据集上最新的目标生成算法，通过达到更多优质的候选人集。
Fast IPv6 scanning is challenging in the field of network measurement as it requires exploring the whole IPv6 address space but limited by current computational power. Researchers propose to obtain possible active target candidate sets to probe by algorithmically analyzing the active seed sets. However, IPv6 addresses lack semantic information and contain numerous addressing schemes, leading to the difficulty of designing effective algorithms. In this paper, we introduce our approach 6VecLM to explore achieving such target generation algorithms. The architecture can map addresses into a vector space to interpret semantic relationships and uses a Transformer network to build IPv6 language models for predicting address sequence. Experiments indicate that our approach can perform semantic classification on address space. By adding a new generation approach, our model possesses a controllable word innovation capability compared to conventional language models. The work outperformed the state-of-the-art target generation algorithms on two active address datasets by reaching more quality candidate sets.
