目标网络场景自适应的IP定位框架

doi:10.11959/j.issn.2096-109x.2023084

网络与信息安全学报 ›› 2023, Vol. 9 ›› Issue (6): 71-85.doi: 10.11959/j.issn.2096-109x.2023084

• 学术论文 • 上一篇

目标网络场景自适应的IP定位框架

祖铄迪¹^,², 丁世昌¹^,², 袁福祥¹^,², 罗向阳¹^,²

¹ 网络空间态势感知河南省重点实验室，河南郑州 450001
² 信息工程大学，河南郑州 450001

修回日期:2023-05-01 出版日期:2023-12-01 发布日期:2023-12-01
作者简介:祖铄迪（1994- ），男，河北保定人，网络空间态势感知河南省重点实验室博士生，主要研究方向为网络空间资源测绘和IP定位
丁世昌（1990- ），男，山东日照人，博士，网络空间态势感知河南省重点实验室讲师，主要研究方向为网络空间资源测绘和IP定位
袁福祥（1991- ），男，山东济宁人，博士，网络空间态势感知河南省重点实验室讲师，主要研究方向为网络空间资源测绘和IP定位
罗向阳（1978- ），男，湖北荆门人，博士，网络空间态势感知河南省重点实验室教授、博士生导师，主要研究方向为网络空间安全
基金资助:
国家自然科学基金(1804263);国家自然科学基金(62172435);国家重点研发计划(2022YFB3102904);中原科技创新领军人才计划(214200510019);河南省科技攻关项目(222102210036);河南省科技攻关项目(232102211052);河南省重点研发与推广专项(232102211052)

Adaptive IP geolocation framework for target network scenarios

Shuodi ZU¹^,², Shichang DING¹^,², Fuxiang YUAN¹^,², Xiangyang LUO¹^,²

¹ Key Laboratory of Cyberspace Situation Awareness of Henan Province, Zhengzhou 450001, China
² Information Engineering University, Zhengzhou 450001, China

Revised:2023-05-01 Online:2023-12-01 Published:2023-12-01
Supported by:
The National Natural Science Foundation of China(1804263);The National Natural Science Foundation of China(62172435);The National Key R＆D Program of China(2022YFB3102904);Zhongyuan Science and Technology Innovation Leading Talent Project(214200510019);The Key Science and Technology Project of Henan Province(222102210036);The Key Science and Technology Project of Henan Province(232102211052);Henan Province Key Research, Development and Promotion Project(232102211052)

摘要/Abstract

摘要：

通过 IP 来定位目标位置是基于位置的服务的重要基础。当前研究者面向不同的网络场景，提出了不同实现原理的IP定位方法。但在不同的网络场景中，各类IP定位方法难以维持理想的定位效果。介绍了基于网络测量的 IP 定位的 3 种典型实现原理，分析了各类方法在不同网络场景下的优缺点，并提出了一种目标网络场景自适应的 IP 定位框架。该定位框架将目标在地标库中进行位置对比，初步判定目标的城市级位置并分布式部署探测源；随后获取目标所在城市的时延、拓扑、同子网地标等属性，并判定其所处网络场景；根据目标网络场景，选择合适类型的定位算法对目标 IP 进行定位，输出位置估计结果。通过在我国11个城市进行模拟定位实验，得到了在不同数量和分布的地标数据支撑下各类IP定位方法的多层次表现，结果显示所提框架的城市级定位成功率可达96.16%，街道级定位中值误差可达4.13 km，同时所提框架在不同的同子网地标数量和目标可达性条件下均能保持稳定的定位效果。实验结果验证了所提框架的有效性，并为IP定位研究提供新的思路。

关键词: IP定位, 网络场景, 网络测量, 网络拓扑, 网络地标

Abstract:

The target location can be determined through IP geolocation, serving as a vital foundation for location-based services.Researchers have proposed various IP geolocation algorithms with different implementation principles to cater to different network scenarios.However, maintaining an ideal geolocation effect proves challenging for these algorithms in diverse network scenarios.Three typical implementation principles for IP geolocation based on network measurement were introduced.The advantages and disadvantages of these methods in various network scenarios were analyzed, and an adaptive IP geolocation framework specifically tailored to target network scenarios was proposed.The geolocation framework was functioned as follows: initially, a preliminary city-level location estimation was obtained by comparing the target’s location with the landmark database.Then, detection sources were deployed in a distributed manner, and information such as delay, topology and same subnet landmarks for the target city was gathered to determine the network scenario.Finally, an appropriate geolocation method was employed to accurately estimate the target’s location according to the identified network scenario.Through simulation geolocation experiments in 11 cities of China, the multi-level performance of various IP geolocation methods supported by landmark data of different quantities and distributions was evaluated.The results indicate that the proposed framework achieves a city-level geolocation success rate of 96.16% and a median error of 4.13 km for street-level geolocation.Moreover, the framework demonstrates stable geolocation performance across different conditions, including varying same subnet landmark numbers and target accessibility.These experimental findings validate the effectiveness of the proposed framework and offer novel insights for IP geolocation research.

Key words: IP geolocation, network scenario, network measurement, network topology, network landmark

中图分类号:

TP393

祖铄迪, 丁世昌, 袁福祥, 罗向阳. 目标网络场景自适应的IP定位框架[J]. 网络与信息安全学报, 2023, 9(6): 71-85.

Shuodi ZU, Shichang DING, Fuxiang YUAN, Xiangyang LUO. Adaptive IP geolocation framework for target network scenarios[J]. Chinese Journal of Network and Information Security, 2023, 9(6): 71-85.

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实验	探测报文	定位精度	同子网地标数量	目标可达性	城市	算法
实验1		城市级			北京、上海、广州、
实验2		街道级	固定		成都、郑州、武汉	RMP-Geo^[14]、
实验3	ICMP、TCP、UDP、	城市级		固定	郑州、洛阳、南阳、	DDT-Geo^[15]、
实验4	ICMP-Paris、UDP-Paris				周口、许昌、信阳	PBG^[23]、本文框架
实验5		街道级	变化		郑州
实验6			固定	变化

地标数量	定位误差/km
地标数量	RMP-Geo	DDT-Geo	PBG	本文框架
1	18.39	17.39	14.06	16.29
2	16.56	12.77	13.82	12.04
5	12.43	9.41	13.89	8.16
10	10.86	8.47	14.63	6.32
20	8.68	8.16	14.42	5.99
50	7.38	6.95	14.10	4.52
75	6.53	6.29	13.20	3.99
100	5.98	5.82	13.01	3.24
150	5.88	4.79	13.64	2.46
200	5.22	4.16	13.79	2.05

目标可达占比	定位误差/km
目标可达占比	RMP-Geo	DDT-Geo	PBG	本文框架
100%	5.96	4.19	12.82	4.01
80%	6.56	4.60	13.89	4.07
60%	7.34	5.10	13.52	3.66
40%	9.31	5.68	12.33	3.88
20%	12.14	6.41	13.02	4.07
0	14.53	9.09	13.10	3.94

目标可达占比	定位成功率
目标可达占比	RMP-Geo	DDT-Geo	PBG	本文框架
100%	91.8%	91.8%	97.6%	99.8%
80%	85.4%	85.4%	97.1%	99.8%
60%	80.8%	80.8%	95.2%	99.1%
40%	73.4%	73.4%	94.7%	98.4%
20%	69.8%	69.8%	94.1%	98.2%
0	64.3%	64.3%	92.5%	97.4%

目标网络场景自适应的IP定位框架

Adaptive IP geolocation framework for target network scenarios

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