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大数据驱动的网络目标定位及跟踪技术

项目来源

国家自然科学基金(NSFC)

项目主持人

罗向阳

项目受资助机构

中国人民解放军战略支援部队信息工程大学

项目编号

U1636219

立项年度

2016

立项时间

未公开

研究期限

未知 / 未知

项目级别

国家级

受资助金额

247.00万元

学科

联合基金领域-电子信息领域

学科代码

L-L05

基金类别

联合基金项目-重点支持项目-NSFC-通用技术基础研究联合基金

关键词

非协作 ; 网络实体 ; 目标定位 ; 跟踪技术 ; 大数据 ; Network entity ; Target geolocation ; Tracking technology ; Big data ; Non-cooperation

参与者

王骞；罗军勇；帅猛；刘琰；尹美娟；邹勤；梁玉；赵帆；丁世昌

参与机构

武汉大学；中国人民解放军信息工程大学；中国通用技术研究院

项目标书摘要：网络目标定位及跟踪技术可广泛用于确定网络敏感目标的地理位置并对其活动轨迹进行追踪，具有重要现实意义和研究价值。现有网络目标定位与跟踪技术主要通过查询IP地址注册信息或进行简单测量确定目标位置，通过大规模部署监测设备跟踪目标，在互联网环境下，尤其是非协作条件下，其定位精度、可靠性和跟踪成本等均难以满足实用需求。本课题拟对基于大数据驱动的目标定位和跟踪关键技术展开研究，重点研究基于网络数据挖掘的地标获取方法、基于网络坐标系的时延预测方法、基于PoP划分和网络社区发现的目标周边区域网络拓扑结构分析方法、适用于不同网络环境的目标IP定位算法，并探索非协作条件下的移动网络目标低成本跟踪技术。课题的开展有望在大批量的高可靠网络地标挖掘、目标周边区域网络拓扑分析、网络目标实体定位算法和基于有限监测设备和社交软件的移动网络目标跟踪等方面取得突破，从而为网络目标的可靠定位与跟踪提供新的方法手段和技术支持。

Application Abstract: Network targets geolocation and tracking technology can be widely used to determine the location of sensitive network targets and track their trajectory,which has important realistic significance and research value.The existing network target geolocation and tracking technology determine the location of targets mainly by querying the IP address registration information or simple measuring,and track the target by deploying amount of monitoring devices.For the Internet environment,the positioning accuracy,reliability and tracking costs of the existing methods are difficult to meet the practical needs,especially under the condition of non-cooperation.This project focuses on the key issues of target geolocation and tracking driven by big data,which mainly includes the landmark acquisition method based on network data mining,delay prediction method based on network coordinates system,network topology analysis method of target surrounding area based on PoP division and network community discovery,IP geolocation algorithms applicable to different type of network environment,and explore the technology of mobile network target tracking,which applicable to non-cooperative conditions at a low cost.It is expected to make a breakthrough in terms of large quantity landmarks mining with high reliability,network topology analysis of target surrounding area,network entities target localization algorithms and targets tracking based on limited monitoring devices and social network software,which can provide some new methods and technical assistance for reliable geolocation and tracking of network targets.

项目受资助省

河南省

项目结题报告(全文)

网络目标定位及跟踪技术广泛用于确定网络敏感目标的地理位置并对其活动轨迹进行追踪，开展相关研究具有重要现实意义和研究价值。依据任务书要求，项目根据各年度计划严格执行，对大数据驱动的目标定位和跟踪关键技术和相关科学问题展开了较为深入的研究。项目重点研究了基于网络数据挖掘的地标获取方法、基于网络坐标系的时延预测方法、基于目标周边区域的网络拓扑分析、适用于互联网的目标定位算法、非协作条件下的移动网络目标跟踪，提出系列相关算法，相关结果共发表学术论文76篇，其中,SCI收录期刊论文49篇,CCF A类国际会议及期刊论文38篇,IEEE/ACM Trans论文23篇。在科学出版社出版首部《网络空间测绘》专著。申请国家技术发明专利27项，其中授权13项。研发的网络目标定位系统在多家相关职能部门得到实际应用，取得显著社会效益。项目执行期间培养了一支以“国防卓青”、“国家优青”、“中原领军人才”为代表的人才队伍，指导毕业博士10名，硕士20名,7人次获河南省、ACM郑州分会和大学优博/优硕学位论文。项目实现了任务书要求的全部研究目标，在研究成果和考核指标上，超额完成了任务书的要求。

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  • 2.Geolocating a WeChat user based on the relation between reported and actual distance

    • 关键词：
    • Internet of Things security; location-based social networks; statisticalcharacteristics; optimization parameters; stepwise strategies; WeChatuser geolocation;PROPAGATION; INTERNET; PRIVACY

    The combination of social networks and the Internet of Things has raised a new wave of network technology application. However, the presence of malicious social network users poses a potential threat to Internet of Things security, and the research on social network user geolocation technology is of great significance. The accuracy of existing geolocation methods for WeChat users depends on the stable correspondence between the reported distance and actual distance. In view of the difficulty to pinpoint users' location in the real world due to WeChat location protection strategy, a WeChat user geolocation algorithm based on the reported and actual distance relation analysis is proposed. The proposed algorithm selects the target reported distance and determines the initial target space based on statistical characteristics of the relation between the reported distance and actual distance. What is more, stepwise strategies are taken to improve the accuracy rate of space partition. Experimental results show that, on the premise that target users can be discovered, the proposed algorithm could achieve higher accuracy compared with the classical space partition-based algorithm and the heuristic number theory based algorithm. The highest geolocation accuracy is within 10m and 56% of geolocation results are within 60m.

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