[2]S. Ding, F. Zhao, and X. Luo, “A street-level IP geolocation method based on delay-distance correlation and multilayered common routers,” Secur. Commun. Netw., vol. 2021, no. 1, pp. 1–10, 2021.
智能设备的地理位置可以帮助提供多媒体内容提供商和5G网络中的用户之间的认证信息。IP地理定位方法可以帮助估计这些智能设备的地理位置。
现有IP地理定位方法的两个关键假设如下:
(1)最小的相对延迟来自最近的主机;
(2)共享最近的公共路由器的主机之间的距离小于其他路由器。
然而,这两个假设在弱连接网络中并不总是正确的,这可能会影响精度。
我们提出了一种新的基于延迟-距离相关性和多层公共路由器的街道级IP地理定位算法(Corr-SLG)。
Corr-SLG的第一个关键思想是基于相对延迟-距离相关性将地标划分为不同的组。与以往的方法不同,Corr-SLG基于强负相关组的最大相对延迟对宿主进行地理定位。
第二个关键思想是将共享多层公共路由器的地标引入到地理定位过程中,而不是仅仅依赖于最近的公共路由器。
此外,为了增加地标的数量,本文还提出了一种新的街道级地标收集方法WiFi地标。在中国省会城市郑州进行的实验表明,Corr-SLG在现实网络中可以显著提高地理定位精度。
1. Introduction
随着智能移动设备(如手机和平板电脑)上的在线视频和远程会议等移动多媒体服务的快速增长,第五代(5G)移动和无线通信系统在全球各地对[1–3]有着巨大的需求。如何管理5G网络中用户与多媒体内容提供商之间的信任关系是[4]面临的一个重要问题。[4]等之前的研究都指出,用户或内容提供商的地理位置是检测未经身份验证或恶意设备的重要信息。IP地理定位可以根据其IP地址[5]找到互联网主机和智能设备的地理位置。除了身份验证之外,IP地理定位还有助于为执法组织和政府机构[6]识别网络攻击或在线欺诈的地理位置。
[1] X. Zhang and Q. Zhu, “Information-centric virtualization for software-defined statistical qos provisioning over 5 g multimedia big data wireless networks,” IEEE Journal on Selected Areas in Communications, vol. 37, no. 8, pp. 1721–1738, 2019. (以信息为中心的虚拟化,用于5g多媒体大数据无线网络上的软件定义的统计质量配置)
[2] X. Ge, H. Wang, R. Zi, Q. Li, and Q. Ni, “5g multimedia massive mimo communications systems,” Wireless Communications and Mobile Computing, vol. 16, no. 11, pp. 1377–1388, 2016. (5g多媒体海量mimo通信系统)
[3] Y. Kang, C. Kim, D. An, and H. Yoon, “Multipath transmission control protocol–based multi-access traffic steering solution for 5 g multimedia-centric network: design and testbed system implementation,” International Journal of Distributed Sensor Networks, vol. 16, no. 2, Article ID 1550147720909759, 2020. (基于多路传输控制协议的5g多媒体中心网络多址流量导向解决方案:设计与测试台系统的实现)
[4] Y. Zhang, R. Deng, E. Bertino, and D. Zheng, “Robust and universal seamless handover authentication in 5 g hetnets,” IEEE Transactions on Dependable and Secure Computing, vol. 99, p. 1, 2019. (5g异位网中的健壮和通用的无缝切换认证)
[5] C. Liu, X. Luo, F. Yuan, and F. Liu, “Rnbg: a ranking nodes based ip geolocation method,” in Proceedings of the IEEE INFOCOM 2020-IEEE Conference On Computer Communications Workshops (INFOCOM WKSHPS), pp. 80–84, IEEE, Toronto, ON, Canada, 2020. (Rnbg:一种基于ip地理定位的节点排序方法)
[6] Z. Wang, H. Li, Q. Li, W. Li, H. Zhu, and L. Sun, “Towards ip geolocation with intermediate routers based on topology discovery,” Cybersecurity, vol. 2, no. 1, pp. 1–14, 2019. (面向基于拓扑发现的中间路由器的ip地理定位)
现有的IP地理定位方法按精度可分为两种:城市级IP地理定位和街道级IP地理定位。城市一级的IP地理定位旨在找到目标IP所在的城市。主要城市级IP地理定位方法的中值误差距离在数十到数百公里之间。在获取城市级位置信息后,可以使用街道级IP地理定位方法找到目标IP所在的特定街道、社区或组织,其中的中值误差距离通常小于10公里。
城市级的IP地理定位已经发展成为一个相对成熟的阶段。主要方法包括GeoPing[7],CBG [8],TBG [9]、Octant[10]、Structon[11]、GeoGet [12]、Chen-Geo[13],PLAG[14],Yuan-Geo[15]和RNBG [5]。本文主要讨论了街道级IP地理定位方法。这有三种主要的街道IP地理定位方法:Checkin-Geo[16]、Geo-NN [17]和Wang-Geo [18]。对于这三种方法,IP数据库还可以提供街道级位置的一小部分IP地址,很难满足人们的需求[19,20]。
[7] V. N. Padmanabhan and L. Subramanian, “An investigation of geographic mapping techniques for internet hosts,” ACM SIGCOMM Computer Communication Review, vol. 31, no. 4, pp. 173–185, 2001. (互联网主机地理制图技术的研究)
[8] B. Gueye, A. Ziviani, M. Crovella, and S. Fdida, “Constraint-based geolocation of internet hosts,” IEEE/ACM Transactions on Networking, vol. 14, no. 6, pp. 1219–1232, 2006. (互联网主机的基于约束的地理定位)
[9] E. Katz-Bassett, J. P. John, A. Krishnamurthy, D. Wetherall, T. Anderson, and Y. Chawathe, “Towards ip geolocation using delay and topology measurements,” in Proceedings Of the 6th ACM SIGCOMM Conference on Internet Measurement, pp. 71–84, ACM, Rio de Janeiro, Brazil, 2006. (利用延迟和拓扑测量来实现ip地理定位)
[10] B. Wong, I. Stoyanov, and E. G. Sirer, “Octant: a comprehensive framework for the geolocalization of internet hosts,” in Proceedings of NSDI, vol. 7, p. 23, Cambridge, MA, USA, 2007. (Octant:一个针对互联网主机地理定位的综合框架)
[11] C. Guo, Y. Liu, W. Shen, H. J. Wang, Q. Yu, and Y. Zhang, “Mining the web and the internet for accurate ip address geolocations,” in Proceedings of IEEE INFOCOM, pp. 2841– 2845, Rio De Janeiro, Brazil, 2009. (挖掘网络和互联网为准确的ip地址地理定位)
[12] D. Li, J. Chen, C. Guo et al., “Ip-geolocation mapping for moderately connected internet regions,” IEEE Transactions on Parallel and Distributed Systems, vol. 24, no. 2, pp. 381–391, 2013. (针对中等连接的互联网区域的ip-地理位置映射)
[13] J. Chen, F. Liu, T. Wang, X. Luo, F. Zhao, and G. Zhu, “Towards region-level ip geolocation based on the path feature,” in Proceedings of the Advanced Communication Technology (ICACT), 2015 17th International Conference, pp. 468–471, IEEE, Phoenix Park, Korea, 2015. (面向基于路径特征的区域级ip地理定位方向)
[14] M. Fotouhi, A. Anand, and R. Hasan, “Plag: practical landmark allocation for cloud geolocation,” in Proceedings of the 2015 IEEE 8th International Conference on Cloud Computing, pp. 1103–1106, IEEE, Washington, DC, USA, 2015. (Plag:云地理定位的实用地标分配)
[15] F. Yuan, F. Liu, R. Xu, Y. Liu, and X. Luo, “Network topology boundary routing ip identification for ip geolocation,” in Proceedings of the International Conference On Artificial Intelligence And Security, pp. 534–544, Springer, Cairo, Egypt, 2020. (ip地理定位的网络拓扑边界路由ip识别)
[16] H. Liu, Y. Zhang, Y. Zhou, D. Zhang, X. Fu, and K. Ramakrishnan, “Mining checkins from location-sharing services for client-independent ip geolocation,” in Proceedings of IEEE INFOCOM, pp. 619–627, Toronto, ON, Canada, 2014. (从位置共享服务中挖掘检查程序,为客户端独立的ip地理位置挖掘)
[17] H. Jiang, Y. Liu, and J. N. Matthews, “Ip geolocation estimation using neural networks with stable landmarks,” in 10 Security and Communication NetworksProceedings of 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pp. 170–175, IEEE, San Francisco, CA, USA, 2016. (利用具有稳定地标的神经网络进行Ip地理定位估计)
[18] Y. Wang, D. Burgener, M. Flores, A. Kuzmanovic, and C. Huang, “Towards street-level client-independent ip geolocation,” in Proceedings of NSDI, vol. 11, p. 27, Boston, MA, USA, 2011. (面向独立于街道级客户端的ip地理定位)
[19] Y. Shavitt and N. Zilberman, “A geolocation databases study,” IEEE Journal on Selected Areas in Communications, vol. 29, no. 10, pp. 2044–2056, 2011. (地