标题： 6视觉：少种子场景下的基于图像编码的IPv6目标生成 显示英文标题

标题： 6Vision: Image-encoding-based IPv6 Target Generation in Few-seed Scenarios

摘要： 高效全球互联网扫描对于网络测量和安全分析至关重要。 尽管现有的目标生成算法在大规模检测中表现出色，但在少量种子地址的情况下，其效率显著下降。 这种下降主要是由于种子地址的复杂配置规则和采样偏差。 此外，BGP前缀具有少量种子地址的情况普遍存在，占所有情况的63.65%。 我们引入了6Vision作为解决这一挑战的方案，通过将IPv6地址编码为图像的新方法，促进对复杂配置规则的全面分析。 通过特征拼接过程，6Vision不仅提高了可学习特征，还整合了与配置模式相关的地址以增强学习效果。 此外，它集成了一个环境反馈机制，根据识别出的活动地址来优化模型参数，从而缓解种子地址固有的采样偏差。 结果表明，即使在少量种子地址的情况下，6Vision也能实现高精度检测。 与现有算法相比，6Vision的HitRate显著提高，范围从181%到2,490%，而CoverNum增加了1.18到11.20倍。 此外，6Vision可以作为现有算法的初步检测模块，产生242%到2,081%的转换增益（CG）。 最终，我们在少量种子地址的情况下实现了28.97%的转换率（CR）。 我们开发了IPv6命中列表补丁，增强了当前的目标生成算法以进行大规模地址检测，从而有效支持IPv6网络测量和安全分析。 显示英文摘要

摘要： Efficient global Internet scanning is crucial for network measurement and security analysis. While existing target generation algorithms demonstrate remarkable performance in large-scale detection, their efficiency notably diminishes in few-seed scenarios. This decline is primarily attributed to the intricate configuration rules and sampling bias of seed addresses. Moreover, instances where BGP prefixes have few seed addresses are widespread, constituting 63.65% of occurrences. We introduce 6Vision as a solution to tackle this challenge by introducing a novel approach of encoding IPv6 addresses into images, facilitating comprehensive analysis of intricate configuration rules. Through a process of feature stitching, 6Vision not only improves the learnable features but also amalgamates addresses associated with configuration patterns for enhanced learning. Moreover, it integrates an environmental feedback mechanism to refine model parameters based on identified active addresses, thereby alleviating the sampling bias inherent in seed addresses. As a result, 6Vision achieves high-accuracy detection even in few-seed scenarios. The HitRate of 6Vision shows a significant improvement ranging from 181% to 2,490% compared to existing algorithms, while the CoverNum increases by a factor of 1.18 to 11.20 times. Additionally, 6Vision can function as a preliminary detection module for existing algorithms, yielding a conversion gain (CG) ranging from 242% to 2,081%. Ultimately, we achieve a conversion rate (CR) of 28.97% for few-seed scenarios. We develop the IPv6 hitlist Patch, which augments current target generation algorithms for large-scale address detection, thereby effectively supporting IPv6 network measurement and security analysis.