User Behavior Detection Based on Statistical Traffic Analysis for Thin Client Services

Remote desktop connection (RDC) services offer clients access to remote content and services, commonly used to access their working environment. With the advent of cloud-based services, an example use case is that of delivering virtual PCs to users in WAN environments. In this paper, we aim to analyze common user behavior when accessing RDC services. We first identify different behavioral categories, and conduct traffic analysis to determine a feature set to be used for classification purposes. We then propose a machine learning approach to be used for classifying behavior, and use this approach to classify a large number of real-world RDCs. Obtained results may be applied in the context of network resource planning, as well as in making Quality of Experience-driven resource allocation decisions.