Design and implementation of a multi-entity knowledge graph for correlation analysis in mobile internet

doi:10.12267/j.issn.2096-5931.2025.03.012

Abstract

Abstract:

By analyzing the relational characteristics among different entities such as APPs, SDKs, and developers, a multi-entity knowledge graph is designed and implemented. This approach proposes relationship mining and correlation analysis methods based on the multi-entity knowledge graph, offering new research perspectives and technical solutions for typical security scenarios such as user privacy data flow and risk APP traceability.

Key words: knowledge graph, mobile internet, correlation analysis

CLC Number:

WANG Songhe, WANG Yanhong, LI Jingdian, WU Linna. Design and implementation of a multi-entity knowledge graph for correlation analysis in mobile internet[J]. Information and Communications Technology and Policy, 2025, 51(3): 89-96.

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URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2025.03.012

http://ictp.caict.ac.cn/EN/Y2025/V51/I3/89

Figures/Tables 6

References 12

[1]	DONG X, GABRILOVICH E, HEITZ G, et al. Knowledge vault: a web-scale approach to probabilistic knowledge fusion[C]// Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014: 601-610.
[2]	PUJARA J, MIAO H, GETOOR L, et al. Knowledge graph identification[C]// The Semantic Web-ISWC 2013: 12th International Semantic Web Conference. Sydney: Springer Berlin Heidelberg, 2013: 542-557.
[3]	ALLIX K, BISSYANDE T F, KLEIN J, et al. Androzoo: collecting millions of android apps for the research community[C]// Proceedings of the 13th International Conference on Mining Software Repositories, 2016: 468-471.
[4]	LI L, GAO J, KONG P, et al. KnowledgeZooClient: constructing knowledge graph for Android[C]// Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020: 73-78.
[5]	MENG G, XUE Y, SIOW J K, et al. Androvault: constructing knowledge graph from millions of android apps for automated analysis[J]. arXiv Preprint, arXiv:1711.07451, 2017.
[6]	ZHOU H, LI W, ZHANG B, et al. MAKG: a mobile application knowledge graph for the research of cybersecurity[C]// Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction: 6th China Conference, CCKS 2021. Singapore: Springer Singapore, 2021: 321-328.
[7]	BAI Y, CHEN S, XING Z, et al. ArgusDroid: detecting Android malware variants by mining permission-API knowledge graph[J]. Science China Information Sciences, 2023, 66(9):192101.
[8]	MA Z, WANG H, GUO Y, et al. Libradar: fast and accurate detection of third-party libraries in android apps[C]// Proceedings of the 38th International Conference on Software Engineering Companion, 2016: 653-656.
[9]	ZHAN X, FAN L, LIU T, et al. Automated third-party library detection for android applications: are we there yet?[C]// Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering, 2020: 919-930.
[10]	PAN J Z. Resource description framework[M]// Handbook on Ontologies. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.
[11]	BLONDEL D V, GUILLAUME J, LAMBIOTTE R, et al. Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008. DOI:10.1088/1742-5468/2008/10/P10008.
[12]	GHOSH S, HALAPPANAVAR M, TUMEO A, et al. Distributed louvain algorithm for graph community detection[C]// 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2018: 885-895.

属性类型	主要属性
基本信息	应用包名、版本号、开发运营者信息、个人信息处理情况、权限使用情况、第三方共享情况、发布时间、应用分类、年龄分级、历史版本
样本结构化信息	权限申请、服务注册、广播监听、网络访问、Activity调用、敏感API调用、开发者签名、MD5、IPs、域名、第三方SDK
样本安全检测信息	APP安全风险、个人信息安全风险、用户权益保护风险
开发运营者企业信息	法人信息、公司名称、公司类型、成立时间、注册邮箱、运营情况、关联企业

属性类型	主要属性
基本信息	应用包名、版本号、开发运营者信息、个人信息处理情况、权限使用情况、第三方共享情况、发布时间、应用分类、年龄分级、历史版本
样本结构化信息	权限申请、服务注册、广播监听、网络访问、Activity调用、敏感API调用、开发者签名、MD5、IPs、域名、第三方SDK
样本安全检测信息	APP安全风险、个人信息安全风险、用户权益保护风险
开发运营者企业信息	法人信息、公司名称、公司类型、成立时间、注册邮箱、运营情况、关联企业