IPv6 active address detection model based on diffusion model

被引:0
|
作者
Yang, Wei [1 ]
Wang, Qianyi [1 ]
Yao, Yu [1 ]
机构
[1] Northeastern Univ, 195 Innovat Rd, Shenyang 110169, Liaoning, Peoples R China
基金
国家重点研发计划;
关键词
IPv6; address; Deep learning; Diffusion model; Address detection; Network security;
D O I
10.1016/j.comnet.2025.111047
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Cyberspace mapping is of great significance to the research of network security. The current work of cyberspace mapping is mainly based on IPv4 address. Due to the exhaustion of IPv4 address allocation, the world has begun to vigorously promote the deployment of IPv6 address. However, due to the wide range of IPv6 address space, the traditional exhaustive search detection method cannot be applied to IPv6 address detection. In order to find active IPv6 addresses, researchers have proposed to build a target address generation model to generate high-quality candidate target detection address set, so as to provide support for IPv6 address space exploration work. Nowadays, many researchers have proposed IPv6 target address generation models. However, the existing target address generation model still has the problems of low hit rate and single address generation pattern. In order to generate more active and diverse candidate target detection address set, We propose an IPv6 active address detection model based on the diffusion model. First, the collected seed addresses will be divided according to the interface identifier type, and then the divided address set will complete the transformation from discrete data to continuous data. After that, the transformed data will be input into the diffusion model for IPv6 address generation. Finally, alias checking will be performed on the generated addresses to reduce the waste of detection resources. The experimental results show that the IPv6 address generation model based on diffusion model has a higher hit rate than other existing address generation algorithms.
引用
收藏
页数:10
相关论文
共 50 条
  • [31] TCAM organization for IPv6 address lookup
    Pao, D
    7th International Conference on Advanced Communication Technology, Vols 1 and 2, Proceedings, 2005, : 26 - 31
  • [32] Research on IPv6 address configuration for a VANET
    Wang Xiaonan
    Zhong Shan
    JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING, 2013, 73 (06) : 757 - 766
  • [33] Using multi-address generation and duplicate address detection to prevent DoS in IPv6
    Song Guangjia
    Wang Hui
    Wang Hangjun
    IET COMMUNICATIONS, 2019, 13 (10) : 1390 - 1396
  • [34] A Hybrid Address Allocation Algorithm for IPv6
    Murugesan, Raja Kumar
    Ramadass, Sureswaran
    RECENT TRENDS IN NETWORK SECURITY AND APPLICATIONS, 2010, 89 : 509 - 517
  • [35] A novel IPv6 address configuration for a 6LoWPAN-based WBAN
    Wang, Xiaonan
    Chen, Hongbin
    Le, Deguang
    JOURNAL OF NETWORK AND COMPUTER APPLICATIONS, 2016, 61 : 33 - 45
  • [36] IPv6 active address discovery algorithm based on multi-level classification and space modeling
    Li G.
    He L.
    Song G.
    Wang Z.
    Yang J.
    Lin J.
    Gao H.
    Qinghua Daxue Xuebao/Journal of Tsinghua University, 2021, 61 (10): : 1177 - 1185
  • [37] A self-adapting mobile IPv6 model based on MAP
    Gao, TH
    Guo, N
    Zhao, H
    2004 IEEE INTERNATIONAL CONFERNECE ON E-TECHNOLOGY, E-COMMERE AND E-SERVICE, PROCEEDINGS, 2004, : 473 - 476
  • [38] Virus Propagation Model for Wireless Sensor Networks Based on IPv6
    Zhou, Zhinan
    Wang, Wendi
    Li, Yuxia
    INTERNATIONAL JOURNAL OF ONLINE ENGINEERING, 2018, 14 (10) : 117 - 128
  • [39] AHP MODEL FOR TRANSITION FROM IPv4 TO IPv6
    Aydogan, Emel Kizilkaya
    Soylu, M. Yekta
    Gencer, Cevriye
    Cetin, Suna
    Soysal, Murat
    Bektas, Onur
    Yuce, Emre
    Ozturk, Yusuf
    Gokirmak, Yavuz
    Sagiroglu, Seref
    JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY, 2011, 26 (03): : 701 - 709
  • [40] Location-Based IPv6 Address Configuration for Vehicular Networks
    Xiaonan Wang
    Deguang Le
    Hongbin Cheng
    Journal of Network and Systems Management, 2016, 24 : 257 - 284