全球网络攻击的空间分布特征与影响因素研究

doi:10.3969/j.issn.1004-9479.2025.09.20240431

摘要/Abstract

摘要：

随着网络空间对全球经济和国家安全的影响深入，网络攻击等非传统安全威胁手段对国家网络空间安全的影响逐渐显现。本文运用网络空间分析方法，对2022年全球网络攻击进行了深入研究，并揭示其空间格局和联系特征。通过建立网络模型，运用度、平均路径长度、网络密度等方法构建计量模型，对网络攻击的地理空间分布特征进行分析。结果表明：①在地理分布上，网络攻击呈现“从西向东、从北到南”的地理分布，美国、加拿大、英国、爱尔兰、荷兰、法国等是主要的攻击来源区域。易受攻击的目标区域包括英国、德国、法国、荷兰、美国、加拿大、巴西、日本、中国、新加坡及英属维尔京群岛。②在攻击类型和主要来源上，网站钓鱼攻击以美国、英国、德国为主要攻击源，美国是首要攻击来源和目标；恶意软件攻击主要源自英国、美国、德国，美国是首要攻击源；恶意控制攻击以美国、英国、新加坡为主要攻击源，美国是首要攻击来源和目标。③网络攻击具有持续性和趋势性，过去的攻击情况对未来攻击有显著的正向影响，呈现循环累积效应。国家技术创新与网络攻击强度呈负相关，表明技术创新的提升可能引入更复杂和难以预测的网络攻击手段，从而导致网络攻击强度的提升。

关键词: 网络攻击, 网络分析, 空间分布, 影响因素

Abstract:

The impact of cyber-attack on national cyberspace security has gradually increased. This paper uses cyberspace analysis methods to conduct an in-depth study of global cyber-attack in 2022 to reveal their spatial pattern and connection characteristics. The results show that: ① In terms of geographical distribution， cyber-attacks show a full geographical coverage， mainly originating from North America and Western Europe， and the main targets are also in these regions. The US， Canada， the UK， Ireland， the Netherlands， France， etc. are the main source areas of attacks. Vulnerable target areas include the UK， Germany， France， the Netherlands， the US， Canada， Brazil， Japan， China， Singapore and the British Virgin Islands. ② In terms of attack types and main sources， website phishing attacks are mainly from the US， the UK， and Germany， and the US is the primary source and target of attacks. Malware attacks mainly come from the UK， the US， and Germany， with the US being the primary attack source. Malicious control attacks mainly come from the US， the UK， and Singapore， with the US being the primary attack source and target. ③ In terms of attack trends， the GMM estimation results show that cyber-attack are persistent and trend-based, and past attacks have a significant positive impact on future attacks, showing a cyclic cumulative effect. National technological innovation is negatively correlated with the intensity of cyber-attack, indicating that the improvement of technological innovation may introduce more complex and unpredictable means of cyber-attack, thereby leading to an increase in the intensity of cyber-attack.

Key words: cyber-attack, network analysis, spatial distribution, influencing factors

孙阳. 全球网络攻击的空间分布特征与影响因素研究[J]. 世界地理研究, 2025, 34(9): 59-72.

Yang SUN. Research on the spatial distribution characteristics and influencing factors of global cyber-attack[J]. World Regional Studies, 2025, 34(9): 59-72.

图/表 5

参考文献 29

[1]	World Economic Forum. Global Risks Report 2022. https://cn.weforum.org/publications/global-risks-report-2022/, 2022-11-10.
[2]	COMAN V. The use of cyberattacks during traditional armed conflicts. Specific and the Commitment of State Responsibility. Journal of Law and Public Administration, 2022, 8(15): 35-44.
[3]	CYBERSECURITY VENTURES. Cyber Security Report 2022. .
[4]	张自力, 解婷, 李文平."东数西算"成渝枢纽战略分析和示范落地. 大数据, 2023, 9(5): 32-47.
	ZHANG Z, XIE T, Li W. National computing hub built in Chengdu-Chongqing economic circle boosts China's "Channel Computing Resources from the East to the West" project .Big Data Research, 2023, 9(5): 32-47.
[5]	ZHAO G, XU K, XU L. Detecting APT malware infections based on malicious DNS and traffic analysis. IEEE Access, 2015, 3: 1132-1142.
[6]	李兆中, 甄峰, 秦萧.长三角地区城市国际化水平研究: 基于来华外国人手机国际漫游信令数据. 地理研究, 2021, 40(4): 1025-1041.
	LI Z, ZHEN F, QIN X. A study on the level of urban internationalization in the Yangtze River Delta: Based on international roaming mobile signaling data of foreigners in China. Geographical Research, 2021, 40(4): 1025-1041.
[7]	DAS R, GÜNDÜZ M. Analysis of cyber-attacks in IoT-based critical infrastructures . International Journal of Information Security Science, 2020, 8(4): 122-133.
[8]	CHEN Y, HUANG Z, XU S, et al. Correction: Spatiotemporal patterns and predictability of cyberattacks. Plos One, 2015, 10(6): e0131501.
[9]	GARG V, KOSTER T. CAMP L. Cross-country analysis of spambots. Eurasip Information Security, 2013, 1:1-13
[10]	RUGHINIŞ C. Nothing ventured, nothing gained. Profiles of online activity, cyber-crime exposure, and security measures of end-users in European Union. Computers & Security, 2014, 43: 111-125.
[11]	BENDOVSCHI A. Cyber-attacks trends, patterns and security countermeasures. Procedia Economics and Finance, 2015, 28: 24-31.
[12]	修文群. 基于GIS的网络犯罪空间管理系统设计. 地理研究, 2006, 25(5): 939-948.
	XIU W. The design of cybercrime spatial management system based on GIS. Geographical Research, 2006, 25(5): 939-948.
[13]	张梅, 张祖勋, 张剑清,等.分布式智能公安GIS 设计.吉林大学学报:工学版, 2007, 37(2): 424- 428.
	ZHANG M, ZHANG Z, ZHANG J,et al. Designing of distributed intelligence GIS used in public security. Journal of Jilin University (Engineering and Technology Edition), 2007, 37(2): 424-428.
[14]	张龙, 周杨, 施群山, 等.与地理空间紧关联的网络空间地图模型. 信息安全学报, 2018, 3(4):63-72.
	ZHANG L, ZHOU Y, SHI Q, et al. Cyberspace map tightly coupled with geographical space. Journal of Cyber Security, 2018, 3(4):63-72.
[15]	关玉军. 浅谈网络犯罪特点及侦查对策. 网络安全技术与应用, 2015(9): 50.
	GUAN Y. A brief discussion on the characteristics of cyber crime and its investigative countermeasures. Network Security Technology & Application, 2015(9): 50.
[16]	郭启全, 高春东, 郝蒙蒙, 等. 发展网络空间可视化技术支撑网络安全综合防控体系建设.中国科学院院刊, 2020, 35(7): 917-924.
	GUO Q, GAO C, HAO M, et al. Develop visualization technology of cyberspace to support construction of comprehensive prevention and control system of cyber security.Bulletin of Chinese Academy of Sciences, 2020, 35(7): 917-924.
[17]	卓蓉蓉, 李峦峦, 余斌, 等. 21世纪以来西方犯罪地理研究述评.人文地理, 2017, 32(2): 16-22, 36.
	ZHUO R, LI L, YU B, et al. A review of studies on geography of crime in western countries since 2000. Human Geography, 2017, 32(2): 16-22, 36.
[18]	张繁, 谢凡, 江颉. 网络威胁安全数据可视化综述. 网络与信息安全学报, 2018, 4(2):34-39.
	ZHANG F, XIE F, JIANG J. Survey on the visualization technologies of threatening security data in cyber space. Chinese Journal of Network and Information Security, 2018, 4(2): 34-39.
[19]	沈逸, 江天骄. 网络空间的攻防平衡与网络威慑的构建. 世界经济与政治, 2018(2): 49-70.
	SHEN Y, JIANG T. Offense-defense balance in cyberspace and a proposed model of cyber deterrence. World Economics and Politics, 2018(2): 49-70.
[20]	吴宗柠, 樊瑛. 复杂网络视角下国际贸易研究综述. 电子科技大学学报, 2018, 47(3):469-480.
	WU Z, FAN Y. Review of international trade: The complex network approach. Journal of University of Electronic Science and Technology of China, 2018, 47(3): 469-480.
[21]	罗桑扎西, 甄峰, 张姗琪. 复杂网络视角下的城市人流空间概念模型与研究框架. 地理研究, 2021, 40(4): 1195-1208.
	LUO S, ZHEN F, ZHANG S. A conceptual model and methodological framework for examining urban people flow space based on complex network perspective. Geographical Research, 2021, 40(4): 1195-1208.
[22]	方叶林, 苏雪晴, 黄震方, 等. 中国东部沿海五大城市群旅游流网络的结构特征及其韧性评估: 基于演化韧性的视角. 经济地理, 2022, 42(2): 203-211.
	FANG Y, SU X, HUANG Z, et al. Structural characteristics and resilience evaluation of tourism flow networks in five major urban agglomerations in coastal China: From the perspective of evolutionary resilience. Economic Geography, 2022, 42(2): 203-211.
[23]	朱晓杰. 国家战略叠加下区域发展的空间关联及影响因素. 区域经济评论, 2023(2):120-127.
	ZHU X. Spatial correlation and influencing factors of regional development under the superposition of national strategies.Regional Economic Review, 2023(2):120-127.
[24]	陈晨, 杨辰, 贾正哲. 基于跨国电视媒体视角的全球城市文化网络研究.国际城市规划, 2022, 37(1):70-77.
	CHEN C, YANG C, JIA Z. Study on cultural network of global cities from the perspective of multinational TV media. Urban Planning International, 2022, 37(1):70-77.
[25]	徐漪, 沈建峰. 网络空间的治理及其演化方向. 产业与科技论坛,2018, 17(11):8-10.
	XU Y, SHEN J. Governance of cyberspace and its evolution .Industrial & Science Tribune, 2018, 17(11):8-10.
[26]	韩梦瑶, 熊焦, 刘卫东. 中国光伏发电的时空分布、竞争格局及减排效益. 自然资源学报, 2022, 37(5): 1338-1351.
	HAN M, XIONG J, LIU W. Spatio-temporal distribution, competitive development and emission reduction of China's photovoltaic power generation. Journal of Natural Resources, 2022, 37(5): 1338-1351.
[27]	杜人淮.新时代优化国防科技工业布局研究.南京政治学院学报,2020, 36(1):17-22.
	DU R. Research on optimizing the layout of national defense science and technology industry in the new era. Front of Thought and Theory, 2020, 36(1):17-22.
[28]	陶锋, 杨文婷, 孙大卫. 地方产业集群、全球生产网络与企业生产率: 基于双重网络嵌入视角.国际经贸探索, 2018, 34(5): 19-34.
	TAO F, YANG W, SUN D. Local industrial cluster,global production network and enterprise productivity: From the perspective of double network embeddedness. International Economics and Trade Research, 2018, 34(5): 19-34.
[29]	蒋海兵, 李业锦. 京津冀地区制造业空间格局演化及其驱动因素. 地理科学进展, 2021, 40(5): 721-735.
	JIANG H, LI Y. Change of spatial structure of manufacturing industry in the Beijing-Tianjin-Hebei region and its driving factors. Progress in Geography, 2021, 40(5): 721-735

解释变量	lnCFS为被解释变量					lnCAF为被解释变量
解释变量	OLS	FE	GMM	FGLS	PCSE	OLS	FE	GMM	FGLS	PCSE
L.logCAF	1.073^***	0.521^***	1.021^***	0.455^***	0.512^***	0.945^***	0.372^***	0.617^***	0.345^***	0.309^***
L.logCFS	－72.580	13.320	148.74	-12.920	-5.750	-73.120	-8.250	-30.980	-16.72	-4.15
logGDP	0.081^**	-0.462^*	0.227^***	-0.624^***	-0.487^*	0.0382	-0.145	0.467^***	-0.089	-0.138
	(-2.450)	(-1.920)	(-7.120)	(-7.320)	(-1.850)	(-1.520)	(-0.920)	(-8.320)	(-0.950)	(-0.930)
logGDSB	0.008	-0.171	0.012^***	-0.169^***	-0.190	-0.008	-0.212^***	0.003^**	-0.148^***	-0.178^**
	(-1.020)	(-1.680)	(-22.560)	(-6.070)	(-1.330)	(-1.420)	(-3.070)	(-2.150)	(-4.600)	(-2.690)
logRD	-0.019 8	0.126^***	-0.087^***	0.134^***	0.121^***	0.015 2	0.009	0.025^***	-0.004	0.009
	(-0.650)	(-3.780)	(-3.850)	(-17.890)	(-3.700)	(-0.770)	(-0.32)	(-3.450)	(-0.940)	(-0.980)
logPOP	0.045^***	0.024 5	0.096^***	-0.007	0.023	0.021 5	0.008	0.035^***	0.007	0.007
	(-3.480)	(-1.170)	(-11.230)	(-0.220)	(-1.580)	(-3.100)	(-0.860)	(-9.650)	(-0.790)	(-0.810)
常数	-0.415	8.111^***	-1.522^***	10.238^***	9.517^***	-0.280	4.246^***	-2.912^***	4.681^***	5.432^***
	(-1.840)	(-3.100)	(-7.780)	(-11.200)	(-3.120)	(-1.720)	(-2.580)	(-7.520)	(-7.75)	(-3.80)
R²	0.965	0.891	―	―	0.973	0.962	0.281	―	―	0.972
F	2 654.120	98.110	―	―	2 112.750	8.450	―	―	―	―
WALD	―	―	117 385	106 544.300	52.920	―	―	2 393.350	60 321.540	198
	―	―	0	0	0	―	―	0	0	0
Hansen-J	―	―	31.500	―	―	―	30.880	―	―	―
	―	-0.948	―	―	―	―	-0.963	―	―	―
AR(1)	―	―	-3.400^***	―	―	―	-4.210^***	―	―	―
AR(2)	―	―	1.120	―	―	―	1.280	―	―	―

解释变量	lnCFS为被解释变量					lnCAF为被解释变量
解释变量	OLS	FE	GMM	FGLS	PCSE	OLS	FE	GMM	FGLS	PCSE
L.logCAF	1.073^***	0.521^***	1.021^***	0.455^***	0.512^***	0.945^***	0.372^***	0.617^***	0.345^***	0.309^***
L.logCFS	－72.580	13.320	148.74	-12.920	-5.750	-73.120	-8.250	-30.980	-16.72	-4.15
logGDP	0.081^**	-0.462^*	0.227^***	-0.624^***	-0.487^*	0.0382	-0.145	0.467^***	-0.089	-0.138
	(-2.450)	(-1.920)	(-7.120)	(-7.320)	(-1.850)	(-1.520)	(-0.920)	(-8.320)	(-0.950)	(-0.930)
logGDSB	0.008	-0.171	0.012^***	-0.169^***	-0.190	-0.008	-0.212^***	0.003^**	-0.148^***	-0.178^**
	(-1.020)	(-1.680)	(-22.560)	(-6.070)	(-1.330)	(-1.420)	(-3.070)	(-2.150)	(-4.600)	(-2.690)
logRD	-0.019 8	0.126^***	-0.087^***	0.134^***	0.121^***	0.015 2	0.009	0.025^***	-0.004	0.009
	(-0.650)	(-3.780)	(-3.850)	(-17.890)	(-3.700)	(-0.770)	(-0.32)	(-3.450)	(-0.940)	(-0.980)
logPOP	0.045^***	0.024 5	0.096^***	-0.007	0.023	0.021 5	0.008	0.035^***	0.007	0.007
	(-3.480)	(-1.170)	(-11.230)	(-0.220)	(-1.580)	(-3.100)	(-0.860)	(-9.650)	(-0.790)	(-0.810)
常数	-0.415	8.111^***	-1.522^***	10.238^***	9.517^***	-0.280	4.246^***	-2.912^***	4.681^***	5.432^***
	(-1.840)	(-3.100)	(-7.780)	(-11.200)	(-3.120)	(-1.720)	(-2.580)	(-7.520)	(-7.75)	(-3.80)
R²	0.965	0.891	―	―	0.973	0.962	0.281	―	―	0.972
F	2 654.120	98.110	―	―	2 112.750	8.450	―	―	―	―
WALD	―	―	117 385	106 544.300	52.920	―	―	2 393.350	60 321.540	198
	―	―	0	0	0	―	―	0	0	0
Hansen-J	―	―	31.500	―	―	―	30.880	―	―	―
	―	-0.948	―	―	―	―	-0.963	―	―	―
AR(1)	―	―	-3.400^***	―	―	―	-4.210^***	―	―	―
AR(2)	―	―	1.120	―	―	―	1.280	―	―	―