计算机科学 ›› 2018, Vol. 45 ›› Issue (1): 200-204.doi: 10.11896/j.issn.1002-137X.2018.01.035

• 网络与通信 • 上一篇    下一篇

基于邻近序列的IP地址地理定位方法

郭立轩,卓子寒,何跃鹰,李强,李舟军   

  1. 北京航空航天大学计算机学院 北京100191,国家计算机网络应急技术处理协调中心 北京100029,国家计算机网络应急技术处理协调中心 北京100029,浪潮北京电子信息产业有限公司高效能服务器和存储技术国家重点实验室 北京100029,北京航空航天大学计算机学院 北京100191
  • 出版日期:2018-01-15 发布日期:2018-11-13
  • 基金资助:
    本文受国家自然科学基金项目(61170189,6,U1636211),国家863计划项目(2015AA016004),北京成像技术高精尖创新中心项目(BAICIT-2016001)资助

IP Geolocation Method Based on Neighbor Sequence

GUO Li-xuan, ZHUO Zi-han, HE Yue-ying, LI Qiang and LI Zhou-jun   

  • Online:2018-01-15 Published:2018-11-13

摘要: IP地址地理定位旨在准确地确定给定的IP地址的物理空间位置,通常采用基于测量的技术或者基于数据分析的技术。现有的基于数据分析的IP地址地理定位技术,对IP地址之间的关系考虑较少。考虑到IP地址的聚集特性,提出了一种基于邻近序列的IP地址地理定位方法。首先计算IP地址的邻近序列,并将其转化为对应的经纬度序列,然后建立模型并求解。以IP地址定位库和含有GPS信息的移动流量数据为原始数据,对该方法进行了实验验证。实验结果表明,通过邻近IP序列确实可以确定IP地址的物理空间位置,平均定位误差在20~30km,实现了区县一级的定位。该方法给IP地址地理定位问题提供了新的解决方案,同时该方法也可以与其他基于测量或者基于数据分析的方法相结合,以获得更优的结果。

关键词: IP地址地理定位,邻近序列,数据分析

Abstract: IP geolocation is intended to accurately determine the physical space location of a given IP address,usually based on measurement technology or data analysis.The existing approaches based on data analysis have less consideration of the relationship between IP address.Taking into account the aggregation of IP address,this paper proposed an IP geolocation approach based on neighbor sequence.First,the approach calculates the neighbor sequence of IP address,converts it to the corresponding sequence of latitude and longitude,and then models it based on the sequence and solves.This approach was experimentally verified by using IP address location library and mobile traffic data with GPS information as original data.Result shows that neighbor sequence can determine the physical space location of IP address,and mean error is between 20km and 30km,which means this approach has achieved county level geolocation.This approach provides a new solution and a new idea for the IP geolocation problem,and it can be combined with other approaches based on measurement or based on data analysis to obtain better result.

Key words: IP geolocation,Neighbour sequence,Data analysis

[1] WANG Z F,FENG J ,XING C Y,et al.Research on the IP geolocation technology[J].Journal of Software,2014,25(7):1527-1540.(in Chinese) 王占丰,冯径,邢长友,等.IP定位技术的研究[J].软件学报,2014,25(7):1527-1540.
[2] PADMANABHAN V N,SUBRAMANIAN L.An investigation of geographic mapping techniques for Internet hosts[J].ACM SIGCOMM Computer Communication Review,2001,31(4):173-185.
[3] KATZ-BASSETT E,JOHN J P,KRISHNAMURTHY A,et al.Towards IP geolocation using delay and topology measurements[C]∥Proceedings of the 6th ACM SIGCOMM Conference on Internet Measurement.ACM,2006:71-84.
[4] ERIKSSON B,BARFORD P,MAGGS B,et al.Posit:a lightweight approach for IP geolocation[J].ACM SIGMETRICS Performance Evaluation Review,2012,40(2):2-11.
[5] DAN O,PARIKH V,DAVISON B D.Improving IP Geolocation using Query Logs[C]∥Proceedings of the Ninth ACM International Conference on Web Search and Data Mining.ACM,2016:347-356.
[6] GUO C X,LIU Y X,SHEN W C,et al.Mining the web and the internet for accurate ip address geolocations[C]∥INFOCOM 2009,IEEE.IEEE,2009:2841-2845.
[7] BACKSTROM L,SUN E,MARLOW C.Find me if you can:improving geographical prediction with social and spatial proximity[C]∥Proceedings of the 19th International Conference on World Wide Web.ACM,2010:61-70.
[8] SHAVITT Y,ZILBERMAN N.A geolocation databases study[J].IEEE Journal on Selected Areas in Communications,2011,29(10):2044-2056.
[9] AL-GADI G,BABIKER A A,MUSTAFA N,et al.Comparison between IPv4 and IPv6 using OPNET simulator[J].IOSR Journal of Engineering (IOSRJEN),2014,4(8):44-50.
[10] ANDREW S T,DAVID J.Wetherall,Computer Networks(Fifth Edition).https://en.wikipedia.org/wiki/IP_address.
[11] ERIKSSON B,BARFORD P,SOMMERS J,et al.A learning-based approach for IP geolocation[C]∥International Confe-rence on Passive and Active Network Measurement.Springer Berlin Heidelberg,2010:171-180.
[12] http://lbsyun.baidu.com/index.php?title=webapi/guide/web-service-geocoding.
[13] ZHANG T.Solving large scale linear prediction problems using stochastic gradient descent algorithms[C]∥Proceedings of the Twenty-first International Conference on Machine Learning.ACM,2004:116.
[14] CNNIC.第37次中国互联网络发展状况统计报告[EB/OL].https://www.cnnic.net.cn.
[15] ZHANG H,LI Z,CHEN Y,et al.Exploit latent dirichlet allocation for one-class collaborative filtering[C]∥Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management.ACM,2014:1991-1994.
[16] WANG S Z,HE L F,STENNETH L,et al.Citywide traffic congestion estimation with social media[C]∥Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems.ACM,2015:34.

No related articles found!
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!