北京主城区伪基站活动时空特征及其影响因素
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摘要
利用北京主城区2017-02-23—04-26的垃圾短信数据反映伪基站的活动轨迹,通过核密度估计分析伪基站的时空特征;以1km格网为研究单元,采用空间计量模型定量分析了建成环境与社会经济因素对伪基站分布的影响。结果发现:1)伪基站活动呈现出明显的时空集聚,空间上东多西少,最大的热点区域位于国贸。2)伪基站活动存在显著的空间自相关性,邻近区域伪基站发送垃圾短信增加1%,相应本区域增加约0.7%。3)建成环境因素中,正向影响最大的是商业和住宅,负向影响最明显的是酒店设施,教育设施存在显著的负空间溢出效应;交通方面,地铁站对伪基站活动的吸引作用仅存在于邻近区域。4)社会经济因素中体现了伪基站活动与低收入群体、人流活动和老龄人口均存在一定正相关性。
China suffers from an increasingly serious threat of SMS spam. The current prevention and control is mainly in the mobile phone to intercept spam messages. In order to suppress the SMS spam effectively, it is necessary to fight against fake base stations(FBSes) from the source. Therefore, it is of great significance to investigate the spatio-temporal distribution and influencing factors of SMS spam sent by FBSes. This study uses the two-month SMS spam data of urban areas in Beijing to reflect the trajectory of FBSes, analyze the temporal and spatial characteristics by kernel density estimation. With the 1km grid as the research unit, we then employed spatial econometric model to quantitatively analysis the effect(including direct and indirect effects) of the built environment and social environment on FBSes. The results show that: 1) The FBSes shows significant space-time agglomeration, and largest hot spot located in the CBD area near Guomao. 2) Because of the diffusivity, the activities of FBSes in a grid increased by about 0.7% for every 1% increase in the average of neighboring grids. 3) Different spatio-temporal distributions of FBSes are influenced by disparate factors, commercial and residential facilities have the most significant positive impacts. The negative impact of hotel facilities is greatest, and there is a certain negative impact from educational facilities, house prices and FBSes. The attraction of subway stations to FBSes in traffic area only exists in the neighboring area. 4) The social and economic factors embody the positive correlation between FBSes and low income group, the flow of people and the aged population. This study expands the quantitative research of FBSes and SMS spam from the perspective of geography, which could potentially help reduce FBSes in the micro-scale of internal urban and provide reference for the management of the city.
China suffers from an increasingly serious threat of SMS spam. The current prevention and control is mainly in the mobile phone to intercept spam messages. In order to suppress the SMS spam effectively, it is necessary to fight against fake base stations(FBSes) from the source. Therefore, it is of great significance to investigate the spatio-temporal distribution and influencing factors of SMS spam sent by FBSes. This study uses the two-month SMS spam data of urban areas in Beijing to reflect the trajectory of FBSes, analyze the temporal and spatial characteristics by kernel density estimation. With the 1km grid as the research unit, we then employed spatial econometric model to quantitatively analysis the effect(including direct and indirect effects) of the built environment and social environment on FBSes. The results show that: 1) The FBSes shows significant space-time agglomeration, and largest hot spot located in the CBD area near Guomao. 2) Because of the diffusivity, the activities of FBSes in a grid increased by about 0.7% for every 1% increase in the average of neighboring grids. 3) Different spatio-temporal distributions of FBSes are influenced by disparate factors, commercial and residential facilities have the most significant positive impacts. The negative impact of hotel facilities is greatest, and there is a certain negative impact from educational facilities, house prices and FBSes. The attraction of subway stations to FBSes in traffic area only exists in the neighboring area. 4) The social and economic factors embody the positive correlation between FBSes and low income group, the flow of people and the aged population. This study expands the quantitative research of FBSes and SMS spam from the perspective of geography, which could potentially help reduce FBSes in the micro-scale of internal urban and provide reference for the management of the city.
引文
Anselin L,Bera A K,Florax R and Yoon M J.1996.Simple diagnostic tests for spatial dependence.Regional Science&Urban Economics,26(1):77-104.
Bivand R.2006.Implementing Spatial Data Analysis Software Tools in R.Geographical Analysis,38(1):23-40.
Center for International Earth Science Information Network-CIESIN-Columbia University.2017.Gridded Population of the World,Version4(GPWv4):Population Density,Revision 10.Palisades,NY:NASASocioeconomic Data and Applications Center(SEDAC).
Chen Z.2014.Malicious base station and detecting malicious base station signal.China Communications,11(8):59-64.
ChinaVis.2017.ChinaVis Data Challenge.[2018-10-08].http://chinavis.org/2017/challenge.html.
Cracolici M F and Uberti T E.2009.Geographical distribution of crime in Italian provinces:a spatial econometric analysis.Jahrbuch Für Regionalwissenschaft,29(1):1-28.
Delany S J,Buckley M and Greene D.2012.SMS spam filtering:Methods and data.Expert Systems with Applications,39(10):9899-9908.
ELHORST J P.2014.Spatial Econometrics From Cross-Sectional Data to Spatial Panels.Berlin Heidelberg:Springer.
互联网安全中心.2016.2016年中国伪基站短信研究报告.[2016-04-21].http://zt.360.cn/1101061855.php?dtid=1101061451&did=1101741409,.[Qihu Internet Security Center.2016.2016 Chinese pseudo base station SMS message Research Report.[2016-04-21].http://zt.360.cn/1101061855.php?dtid=1101061451&did=1101741409.]
黄良友.2010.垃圾短信治理对策研究.重庆邮电大学学报(社会科学版),22(3):25-30.[Huang Liangyou.2010.On the Countermeasures of Junk Message.Journal of Chongqing University of Posts and Telecommunications(Social Science Edition),22(3):25-30.]
姜超,唐焕丽,柳林.2014.中国犯罪地理研究述评.地理科学进展,33(4):561-573.[Jiang Chao,Tang Huanli and Liu Lin.2014.Review of crime geography in China.Progress in Geography,33(4):561-573.]
Kopczewska K,Kud?a J and Walczyk K.2017.Strategy of Spatial Panel Estimation:Spatial Spillovers Between Taxation and Economic Growth.Applied Spatial Analysis&Policy,10(1):77-102.
Lesage J and Fischer M M.2011.Estimates of the Impact of Static and Dynamic Knowledge Spillovers on Regional Factor Productivity.International Regional Science Review,35(1):103-127.
Lesage J and Pace R K.2009.Introduction to Spatial Econometrics.Boca Raton:Chapman&Hall/CRC,513-514.
Levin N and Zhang Q.2017.A global analysis of factors controlling VIIRSnighttime light levels from densely populated areas.Remote Sensing of Environment,190:366-382.
Li Z,Wang W,Wilson C,Chen J,Qian C,Jung T,Zhang L,Liu K,Li Xand Liu Y.2017.FBS-Radar:Uncovering Fake Base Stations at Scale in the Wild.San Diego,California:Network and Distributed System Security Symposium.
李业锦,朱红.2013.北京社会治安公共安全空间结构及其影响机制--以城市110警情为例.地理研究,32(5):870-880.[Li Yejin and Zhu Hong.2013.Spatial distribution and influencing mechanism of social and public security:An urban security spatial analysis based on from city crime alarm data.Geographical Research,32(5):870-880.]
柳林,姜超,周素红,刘凯,徐冲,曹晶晶.2017.城市入室盗窃犯罪的多尺度时空格局分析--基于中国H市DP半岛的案例研究.地理研究,36(12):2451-2464.[Liu Lin,Jiang Chao,Zhou Suhong,Liu Kai,Xu Chong and Cao Jingjing.2017.Spatial-temporal patterns of burglary at multiple scales:The case of DP peninsula in H city,China.Geographical Research,36(12):2451-2464.]
龙冬平,柳林,周素红,杜方叶,宋广文,肖露子.2017.地理学视角下犯罪者行为研究进展.地理科学进展,36(7):886-902.[Long Dongping,Liu Lin,Zhou Suhong,Du Fangye,Song Guangwen and Xiao Luzi.2017.Research Progress of Criminal Behavior from the Perspective of Geography.Progress in Geography,36(7):886-902.]
Murynets I and Jover R P.2012.Crime scene investigation:SMS spam data analysis.Boston MA:Proceedings of the 2012 Internet Measurement Conference,441-452.
Messner S F and Anselin L.2004.Spatial analyses of homicide with areal data.Spatially Integrated Social Science,12:127-144.
Naveed A and Ahmad N.2014.Technology Spillovers and International Borders:A Spatial Econometric Analysis.Journal of Borderlands Studies,31(4):441-461.
蒲誉文,胡海波,何凌君.2018.基于多用户垃圾短信数据的伪基站活动轨迹可视分析方法.计算机应用,38(4):1207-1212.[Pu Yuwen,Hu Haibo and He Lingjun.2018.Visual Analytics on Trajectory of Pseudo Base-Stations based on SMS Spam Collected from Mobilephone Users.Journal of Computer Applications,38(4):1207-1212.]
Song C,Kwan M P,Song W and Zhu J.2017.A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence.Sustainability,9(5):819.
Song Y,Zhou K and Chen X.2012.Fake BTS Attacks of GSM System on Software Radio Platform.Journal of Networks,7(2):275-281.
宋广文,肖露子,周素红,龙冬平,周淑丽,刘凯.2017.居民日常活动对扒窃警情时空格局的影响.地理学报,72(2):356-367.[Song Guangwen,Xiao Luzi,Zhou Suhong,Long Dongping,Zhou Shuli and Liu Kai.2017.Impact of Residents’Routine Activities on the Spatialtemporal Pattern of Theft from Person.Acta Geographica Sinica,72(2):356-367.]
唐楷,赵韦鑫,蒋宏宇,吴亚东,王松.2018.基于可视分析的伪基站活动特征分析方法.西南科技大学学报,33(2):72-78.[Tang Kai,Zhao Weixin,Jiang Hongyu,Wu Yadong and Wang Song.2018.AVisual Analytic Method of Activity Characteristics Exploration for Pseudo Base Station.Journal of Southwest University of Science and Technology,33(2):72-78.]
腾讯安全联合实验室.2018.2017年度互联网安全报告.(2018-01-18)[2018-01-30].https://slab.qq.com/news/authority/1708.html.[Tencent United Security Laboratory.2018.Tencent Security 2017 Internet Security Report.(2018-01-18)[2018-01-30].https://slab.qq.com/news/authority/1708.html.]
汪伟,陶海燕,卓莉,李敏,李旭亮,汪珂丽,史清丽,2018.北京主城区伪基站时空规律分析.地球信息科学学报,20(7):978-987.[Wang Wei,Tao Haiyan,Zhuo Li,Li Min,Li Xuliang,Wang Keli and Shi Qingli.2018.Spatio-temporal analysis of pseudo base stations in Beijing downtown.Journal of Geo-information Science,20(7):978-987.]
王发曾.2012.城市犯罪空间.南京:东南大学出版社.[Wang Fazeng.2012.Urban Crime Space.Nanjing:Southeast University Press.]
王洋,金利霞,张虹鸥,吴康敏,王长建,黄耿志.2017.社会空间视角下广州居住地犯罪风险的格局与模式.地理研究,36(12):2465-2478.[Wang Yang,Jin Lixia,Zhang Hong’ou,Wu Kangmin,Wang Changjian and Huang Gengzhi.2017.Pattern and model of residential criminal risk based on social space in Guangzhou,China.Geographical Research,36(12):2465-2478.]
网络不良与垃圾信息举报受理中心.2017.2016年上半年手机短信状况调查报告.[2017-03-31].https://www.12321.cn/Uploads/pdf/1490948516.pdf.[Unsolicited Electronic Messages Complaining Center.2017.Mobile Phone SMS Survey Report for the First Half of2016.[2017-03-31].https://www.12321.cn/Uploads/pdf/1490948516.pdf.]
吴一平,芮萌.2010.收入分配不平等对刑事犯罪的影响.经济学,10(1):291-310.[Wu Yiping and Rui Meng.2010.The Impact of Income Inequality on Crime.China Economic Quarterly,10(1):291-310.]
严小兵.2013a.上海市就业市场状况与城市犯罪的空间计量分析.人文地理,28(3):85-90.[Yan Xiaobing.2013a.A Spatial Econometric Analysis on Local Labor Market and Crime in Shanghai.Human Geography,28(3):85-90.]
严小兵.2013b.中国省域刑事犯罪率的时空演变及机制研究.地理科学,33(5):529-537.[Yan Xiaobing.2013b.Spatio-temporal Pattern of Crime Rates in China.Scientia Geographica Sinica,33(5):529-537.]
曾剑秋,杨光永,董豪.2015.垃圾短信分类治理对策研究.北京邮电大学学报(社会科学版),17(6):39-44.[Zeng Jianqiu,Yang Guangyong and Dong Hao.2015.Spam SMS Classification Governance Strategies.Journal of Beijing University of Posts and Telecommunications(Social Sciences Edition),17(6):39-44.]
卓蓉蓉,李峦峦,余斌,郑文升.2017.21世纪以来西方犯罪地理研究述评.人文地理,32(2):16-22.[Zhuo Rongrong,Li Luanluan,Yu Bin and Zheng Wensheng.2017.A Review Studies on Geography of Crime in Western Countries since 2000.Human Geography,32(2):16-22.]
Bivand R.2006.Implementing Spatial Data Analysis Software Tools in R.Geographical Analysis,38(1):23-40.
Center for International Earth Science Information Network-CIESIN-Columbia University.2017.Gridded Population of the World,Version4(GPWv4):Population Density,Revision 10.Palisades,NY:NASASocioeconomic Data and Applications Center(SEDAC).
Chen Z.2014.Malicious base station and detecting malicious base station signal.China Communications,11(8):59-64.
ChinaVis.2017.ChinaVis Data Challenge.[2018-10-08].http://chinavis.org/2017/challenge.html.
Cracolici M F and Uberti T E.2009.Geographical distribution of crime in Italian provinces:a spatial econometric analysis.Jahrbuch Für Regionalwissenschaft,29(1):1-28.
Delany S J,Buckley M and Greene D.2012.SMS spam filtering:Methods and data.Expert Systems with Applications,39(10):9899-9908.
ELHORST J P.2014.Spatial Econometrics From Cross-Sectional Data to Spatial Panels.Berlin Heidelberg:Springer.
互联网安全中心.2016.2016年中国伪基站短信研究报告.[2016-04-21].http://zt.360.cn/1101061855.php?dtid=1101061451&did=1101741409,.[Qihu Internet Security Center.2016.2016 Chinese pseudo base station SMS message Research Report.[2016-04-21].http://zt.360.cn/1101061855.php?dtid=1101061451&did=1101741409.]
黄良友.2010.垃圾短信治理对策研究.重庆邮电大学学报(社会科学版),22(3):25-30.[Huang Liangyou.2010.On the Countermeasures of Junk Message.Journal of Chongqing University of Posts and Telecommunications(Social Science Edition),22(3):25-30.]
姜超,唐焕丽,柳林.2014.中国犯罪地理研究述评.地理科学进展,33(4):561-573.[Jiang Chao,Tang Huanli and Liu Lin.2014.Review of crime geography in China.Progress in Geography,33(4):561-573.]
Kopczewska K,Kud?a J and Walczyk K.2017.Strategy of Spatial Panel Estimation:Spatial Spillovers Between Taxation and Economic Growth.Applied Spatial Analysis&Policy,10(1):77-102.
Lesage J and Fischer M M.2011.Estimates of the Impact of Static and Dynamic Knowledge Spillovers on Regional Factor Productivity.International Regional Science Review,35(1):103-127.
Lesage J and Pace R K.2009.Introduction to Spatial Econometrics.Boca Raton:Chapman&Hall/CRC,513-514.
Levin N and Zhang Q.2017.A global analysis of factors controlling VIIRSnighttime light levels from densely populated areas.Remote Sensing of Environment,190:366-382.
Li Z,Wang W,Wilson C,Chen J,Qian C,Jung T,Zhang L,Liu K,Li Xand Liu Y.2017.FBS-Radar:Uncovering Fake Base Stations at Scale in the Wild.San Diego,California:Network and Distributed System Security Symposium.
李业锦,朱红.2013.北京社会治安公共安全空间结构及其影响机制--以城市110警情为例.地理研究,32(5):870-880.[Li Yejin and Zhu Hong.2013.Spatial distribution and influencing mechanism of social and public security:An urban security spatial analysis based on from city crime alarm data.Geographical Research,32(5):870-880.]
柳林,姜超,周素红,刘凯,徐冲,曹晶晶.2017.城市入室盗窃犯罪的多尺度时空格局分析--基于中国H市DP半岛的案例研究.地理研究,36(12):2451-2464.[Liu Lin,Jiang Chao,Zhou Suhong,Liu Kai,Xu Chong and Cao Jingjing.2017.Spatial-temporal patterns of burglary at multiple scales:The case of DP peninsula in H city,China.Geographical Research,36(12):2451-2464.]
龙冬平,柳林,周素红,杜方叶,宋广文,肖露子.2017.地理学视角下犯罪者行为研究进展.地理科学进展,36(7):886-902.[Long Dongping,Liu Lin,Zhou Suhong,Du Fangye,Song Guangwen and Xiao Luzi.2017.Research Progress of Criminal Behavior from the Perspective of Geography.Progress in Geography,36(7):886-902.]
Murynets I and Jover R P.2012.Crime scene investigation:SMS spam data analysis.Boston MA:Proceedings of the 2012 Internet Measurement Conference,441-452.
Messner S F and Anselin L.2004.Spatial analyses of homicide with areal data.Spatially Integrated Social Science,12:127-144.
Naveed A and Ahmad N.2014.Technology Spillovers and International Borders:A Spatial Econometric Analysis.Journal of Borderlands Studies,31(4):441-461.
蒲誉文,胡海波,何凌君.2018.基于多用户垃圾短信数据的伪基站活动轨迹可视分析方法.计算机应用,38(4):1207-1212.[Pu Yuwen,Hu Haibo and He Lingjun.2018.Visual Analytics on Trajectory of Pseudo Base-Stations based on SMS Spam Collected from Mobilephone Users.Journal of Computer Applications,38(4):1207-1212.]
Song C,Kwan M P,Song W and Zhu J.2017.A Comparison between Spatial Econometric Models and Random Forest for Modeling Fire Occurrence.Sustainability,9(5):819.
Song Y,Zhou K and Chen X.2012.Fake BTS Attacks of GSM System on Software Radio Platform.Journal of Networks,7(2):275-281.
宋广文,肖露子,周素红,龙冬平,周淑丽,刘凯.2017.居民日常活动对扒窃警情时空格局的影响.地理学报,72(2):356-367.[Song Guangwen,Xiao Luzi,Zhou Suhong,Long Dongping,Zhou Shuli and Liu Kai.2017.Impact of Residents’Routine Activities on the Spatialtemporal Pattern of Theft from Person.Acta Geographica Sinica,72(2):356-367.]
唐楷,赵韦鑫,蒋宏宇,吴亚东,王松.2018.基于可视分析的伪基站活动特征分析方法.西南科技大学学报,33(2):72-78.[Tang Kai,Zhao Weixin,Jiang Hongyu,Wu Yadong and Wang Song.2018.AVisual Analytic Method of Activity Characteristics Exploration for Pseudo Base Station.Journal of Southwest University of Science and Technology,33(2):72-78.]
腾讯安全联合实验室.2018.2017年度互联网安全报告.(2018-01-18)[2018-01-30].https://slab.qq.com/news/authority/1708.html.[Tencent United Security Laboratory.2018.Tencent Security 2017 Internet Security Report.(2018-01-18)[2018-01-30].https://slab.qq.com/news/authority/1708.html.]
汪伟,陶海燕,卓莉,李敏,李旭亮,汪珂丽,史清丽,2018.北京主城区伪基站时空规律分析.地球信息科学学报,20(7):978-987.[Wang Wei,Tao Haiyan,Zhuo Li,Li Min,Li Xuliang,Wang Keli and Shi Qingli.2018.Spatio-temporal analysis of pseudo base stations in Beijing downtown.Journal of Geo-information Science,20(7):978-987.]
王发曾.2012.城市犯罪空间.南京:东南大学出版社.[Wang Fazeng.2012.Urban Crime Space.Nanjing:Southeast University Press.]
王洋,金利霞,张虹鸥,吴康敏,王长建,黄耿志.2017.社会空间视角下广州居住地犯罪风险的格局与模式.地理研究,36(12):2465-2478.[Wang Yang,Jin Lixia,Zhang Hong’ou,Wu Kangmin,Wang Changjian and Huang Gengzhi.2017.Pattern and model of residential criminal risk based on social space in Guangzhou,China.Geographical Research,36(12):2465-2478.]
网络不良与垃圾信息举报受理中心.2017.2016年上半年手机短信状况调查报告.[2017-03-31].https://www.12321.cn/Uploads/pdf/1490948516.pdf.[Unsolicited Electronic Messages Complaining Center.2017.Mobile Phone SMS Survey Report for the First Half of2016.[2017-03-31].https://www.12321.cn/Uploads/pdf/1490948516.pdf.]
吴一平,芮萌.2010.收入分配不平等对刑事犯罪的影响.经济学,10(1):291-310.[Wu Yiping and Rui Meng.2010.The Impact of Income Inequality on Crime.China Economic Quarterly,10(1):291-310.]
严小兵.2013a.上海市就业市场状况与城市犯罪的空间计量分析.人文地理,28(3):85-90.[Yan Xiaobing.2013a.A Spatial Econometric Analysis on Local Labor Market and Crime in Shanghai.Human Geography,28(3):85-90.]
严小兵.2013b.中国省域刑事犯罪率的时空演变及机制研究.地理科学,33(5):529-537.[Yan Xiaobing.2013b.Spatio-temporal Pattern of Crime Rates in China.Scientia Geographica Sinica,33(5):529-537.]
曾剑秋,杨光永,董豪.2015.垃圾短信分类治理对策研究.北京邮电大学学报(社会科学版),17(6):39-44.[Zeng Jianqiu,Yang Guangyong and Dong Hao.2015.Spam SMS Classification Governance Strategies.Journal of Beijing University of Posts and Telecommunications(Social Sciences Edition),17(6):39-44.]
卓蓉蓉,李峦峦,余斌,郑文升.2017.21世纪以来西方犯罪地理研究述评.人文地理,32(2):16-22.[Zhuo Rongrong,Li Luanluan,Yu Bin and Zheng Wensheng.2017.A Review Studies on Geography of Crime in Western Countries since 2000.Human Geography,32(2):16-22.]