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Insider threat detection approach for tobacco industry based on heterogeneous graph embedding
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作者 季琦 LI Wei +2 位作者 PAN Bailin XUE Hongkai QIU Xiang 《High Technology Letters》 EI CAS 2024年第2期199-210,共12页
In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,t... In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods. 展开更多
关键词 insider threat detection advanced persistent threats graph construction heterogeneous graph embedding
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Heterogeneous Network Embedding: A Survey
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作者 Sufen Zhao Rong Peng +1 位作者 Po Hu Liansheng Tan 《Computer Modeling in Engineering & Sciences》 SCIE EI 2023年第10期83-130,共48页
Real-world complex networks are inherently heterogeneous;they have different types of nodes,attributes,and relationships.In recent years,various methods have been proposed to automatically learn how to encode the stru... Real-world complex networks are inherently heterogeneous;they have different types of nodes,attributes,and relationships.In recent years,various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks(HINs)into low-dimensional embeddings;this task is called heterogeneous network embedding(HNE).Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification,recommender systems,and information retrieval.Here,we provide a comprehensive survey of key advancements in the area of HNE.First,we define an encoder-decoder-based HNE model taxonomy.Then,we systematically overview,compare,and summarize various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories to identify more potentially competitive HNE frameworks.We also summarize the application fields,benchmark datasets,open source tools,andperformance evaluation in theHNEarea.Finally,wediscuss open issues and suggest promising future directions.We anticipate that this survey will provide deep insights into research in the field of HNE. 展开更多
关键词 heterogeneous information networks representation learning heterogeneous network embedding graph neural networks machine learning
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