Enterprise Security with Adaptive Ensemble Learning on Cooperation and Interaction Patterns

Social networking research has primarily focused on public social networking services and applications, while rich social interactions in an enterprise setting and their related context has received less attention. In this paper, we focus on using the enterprise social context to augment traditional authentication tools. This is motivated by the emergence of smart mobile devices which introduce ease of remote access to work from almost anywhere and anytime, adding spatio-temporal dimension to the social context. However, it remains a challenge to efficiently manage access-controlled events by using different contextual properties. This paper analyzes specific actions under specific access-control rules to extract context-aware machine learning predictions. Such analysis includes the introduction of three contextual metrics: document shareability, valuation, and user cooperation. Furthermore, these socially-dependent metrics are combined with our Smart Enterprise Access Control (SEAC) technique to achieve authenticity precision of 99% while improving the corresponding efficiency trade-off associated with high and strict security.