Link completion and key node identification of fraudulent network based on knowledge graph embedding
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.
Knowledge graph embeddingGroups involved in fraudLink predictionKey node identification
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