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- W3096578622 abstract "Insider threat is a well-recognized problem in the cyber-security domain. There is good amount of research on detecting and predicting an insider attack. However, none of them addresses the influence of an insider over other individuals, and the spread of impact due to direct and indirect access to enterprise assets by having such influence. In this work, we propose a graph-based influence profiling solution called rProfiler that analyzes the data from multiple sources to determine the influence spread and calculate the probability of loss of data from an affected device using pertinent graph features. We also highlight multiple enterprise scenarios that may benefit from this work." @default.
- W3096578622 created "2020-11-09" @default.
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- W3096578622 date "2020-10-30" @default.
- W3096578622 modified "2023-09-23" @default.
- W3096578622 title "rProfiler -- Assessing Insider Influence on Enterprise Assets" @default.
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- W3096578622 doi "https://doi.org/10.1145/3372297.3420026" @default.
- W3096578622 hasPublicationYear "2020" @default.
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