Abstract
User authentication over the network builds a foundation of trust within large-scale computer networks. The collection of this network authentication activity provides valuable insight into user behavior within an enterprise network. Representing this authentication data as a set of user-specific graphs and graph features, including time-constrained attributes, enables novel and comprehensive analysis opportunities. We show graph-based approaches to user classification and intrusion detection with practical results. We also show a method for assessing network authentication trust risk and cyber attack mitigation within an enterprise network using bipartite authentication graphs. We demonstrate the value of these graph-based approaches on a real-world authentication data set collected from an enterprise network.
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