四川大学学报（自然科学版）2024，Vol.61Issue(3)：38-48,11.DOI:10.19907/j.0490-6756.2024.030004
基于知识图谱嵌入的涉诈网络链接补全和关键节点识别
Link completion and key node identification of fraudulent network based on knowledge graph embedding
摘要
Abstract
Fraudulent websites,as a common medium for online scams,play a significant role as providers of platform content in cybercrime.This form of criminal activity exhibits a high degree of teamwork and collabo-ration,with fraudulent websites often demonstrating strong interconnections among fraudulent assets.These assets,including fraudulent websites and associated criminal groups,collectively form a vast network of fraudulent.Although numerous researchers have conducted studies on identifying fraudulent websites,re-search on the correlation of fraudulent assets remains relatively scarce.Due to the anonymity of nodes within fraudulent networks,acquiring direct identity information related to fraudulent assets is exceedingly challeng-ing for law enforcement personnel,making it difficult to trace and counter fraudulent websites accurately and promptly.This paper,based on an ontological framework,constructs a fine-grained knowledge graph of fraudulence and innovatively embeds knowledge graphs into the field of tracing fraudulent websites.It ab-stracts relationships within fraudulent networks as rotational operations in multidimensional complex spaces to model entities and relationships within the fraudulent knowledge graph.By utilizing knowledge graph embed-ding vectors,he model to complete entity relationships.Furthermore,this paper innovatively quantifies the de-gree of revelation of fraudulent team identities by relationships within the fraudulent knowledge graph.It opti-mizes centrality algorithms for feature vectors by utilizing weighted fraudulent relationships to unearth key clue nodes within it.Experimental results indicate that the proposed model exhibits a higher accuracy in com-pleting asset relationships.On the dataset containing 37866 entities,the HITS@10 accuracy reached 47%,surpassing other knowledge graph embedding models in effectiveness.Subsequent case studies demonstrate that the key clue mining method designed in this paper can effectively trace the associations of fraudulent as-sets,thereby achieving significant success.关键词
知识图谱嵌入/涉诈团伙/链接预测/关键节点识别Key words
Knowledge graph embedding/Groups involved in fraud/Link prediction/Key node identification分类
信息技术与安全科学引用本文复制引用
李泽卿,黄诚,曾雨潼,冷涛..基于知识图谱嵌入的涉诈网络链接补全和关键节点识别[J].四川大学学报（自然科学版）,2024,61(3):38-48,11.基金项目
智能警务四川省重点实验室开放课题(ZNJW2024KFZD003) （ZNJW2024KFZD003）
国家重点研发项目(2021YFB3100500) （2021YFB3100500）
四川省科技厅应用基础项目(2022NSFSC0752) （2022NSFSC0752）
