兰州理工大学学报

兰州理工大学学报 ›› 2023, Vol. 49 ›› Issue (4): 102-107.

• 自动化技术与计算机技术 • 上一篇    下一篇

基于生成对抗网络的IPv6地址存活性预测

陈勇群*1,2, 陈玉成1,2, 胡华淼1,2, 戴佳浩1,2, 康潆允3, 王巍1,2   

  1. 1.通信信息控制和安全技术重点实验室, 浙江 嘉兴 314033;
    2.中国电子科技集团公司第三十六研究所, 浙江 嘉兴 314033;
    3.沈阳理工大学 信息科学与工程学院, 辽宁 沈阳 110159
  • 收稿日期:2021-10-31 出版日期:2023-08-28 发布日期:2023-08-29
  • 通讯作者: 陈勇群(1987-),男,浙江金华人,博士,高级工程师. Email:490168179@163.com
  • 基金资助:
    国家自然科学基金(U20B2050)

IPv6 address alive prediction based on generative adversarial network

CHEN Yong-qun1,2, CHEN Yu-cheng1,2, HU Hua-miao 1,2, DAI Jia-hao 1,2 KANG Ying-yun3, WANG Wei1,2   

  1. 1. Science and Technology on Communication Information Security Control Laboratory, Jiaxing 314033, China;
    2. China Electronic Corporation No.36 Institute, Jiaxing 314033, China;
    3. School of Information Science and Engineering,Shenyang Ligong University, Shenyang 110159, China
  • Received:2021-10-31 Online:2023-08-28 Published:2023-08-29

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摘要： 为了解决IPv6海量地址空间难以全谱探测的问题,基于IPv6地址命中列表机制和 IPv6单播地址结构,通过公开来源获取了多种渠道的IPv6地址,然后进行采集、清洗和入库,构建了IPv6原始地址集,研究了地址集中稳定与不稳定段之间的依赖关系,提出了一种基于生成对抗网络的IPv6地址存活性预测模型.考虑到IPv6地址各位之间的离散性,设计了确定性二项神经元和随机二项神经元,并采用Sigmoid调整直通估计子来解决二项神经元反向传播计算复杂度过高的问题.在特定AS域中对IPv6地址集进行训练,得到的生成器作为IPv6地址存活性预测模型,生成了地址命中列表.实验分析结果和互联网探测结果表明,所提模型能有效生成新的IPv6地址,并在实际探测中得到ICMPv6响应的概率比顺序或随机全面扫描高;另外,不同网段的探测存活率相差很大,分布区间为0~61%.

关键词: IPv6地址, 生成对抗网络, 命中列表

Abstract: It is difficult to detect the whole IPv6 deployment because of the massive address space. Based on the IPv6 unicast address structure, hitlist mechanism is adopted to solve this problem. First, the IPv6 addresses from public ipv6 address data sources are collected, cleaned and stored in the database according to structure of the original IPv6 address set. After that, the dependency between the stable and unstable segments of the address set is studied. An IPv6 address survivability prediction model based on the generation adversarial network are proposed. By considering the discrete nature of IPv6 addresses structure, deterministic binomial neurons and random binomial neurons are designed, and Sigmoid is used to adjust the pass-through estimator to solve the problem of high computational complexity of the back propagation of binomial neurons. The IPv6 address set is trained in each specific AS domain, and the obtained generator is used as an IPv6 address survivability prediction model to generate an IPv6 address hitlist. Experimental analysis results and internet probe results show that the proposed model can effectively generate new IPv6 addresses, and the probability of getting an ICMPv6 response in actual detection is higher than that of sequential or random scans. The survival rates have great difference from 0 to 61%.

Key words: IPv6 address, generative adversarial network, hitlist

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