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HeteroUI: A Framework Based on Heterogeneous Information Network Embedding for User Identification in Enterprise Networks

机译:hotelSoui:基于异构信息网络嵌入企业网络用户识别的框架

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摘要

User identification process is an important security guard towards discovering insider threat and preventing unauthorized access in enterprise networks. However, most existing user identification approaches based on behavior analysis fail to capture latent correlations between multi-domain behavior records due to the lack of a panoramic view or the disability of dealing with heterogeneous data. In light of this, this paper presents HeteroUI, a framework based on heterogeneous information network embedding for user identification in enterprise networks. In our model, multi-domain heterogeneous behavior records are first transformed into a heterogeneous information network, then the embeddings of entities will be trained iteratively according to a joint objective combining with local and global components for more accurate user identification. Experimental results on the CERT insider threat dataset r4.2 demonstrate that HeteroUI exhibits excellent performance in discovering user identities with the mean average precision reaching over 98%. Besides, HeteroUI has a certain contribution to inferring potential insiders in a multi-user and multi-domain environment.
机译:用户识别过程是一个重要的保安,用于发现内幕威胁并防止企业网络中未经授权的访问。然而,由于缺乏全景视图或处理异构数据的残疾,基于行为分析的大多数现有用户识别方法无法捕获多域行为记录之间的潜在相关性。鉴于此,本文介绍了异质信息网络嵌入企业网络中的用户识别的异构信息网络的框架。在我们的模型中,首先将多域异构行为记录转换为异构信息网络,然后根据与本地和全局组件的联合目标相结合,迭代地验证实体的嵌入,以便更准确的用户识别。 Cerce Insider威胁数据集R4.2上的实验结果表明Heter Oui在发现用户身份中表现出优异的性能,以超过98%的平均平均精度。此外,Hoter Oui对多用户和多域环境推断潜在的内部人具有一定的贡献。

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