Machine Learning (ML) technologies applied to Cybersecurity, especially in the area of network cyber threat detection, are a promising choice, but they require additional research in the applicability of a wide range of available algorithms. Such algorithms usually require training using good-quality and quantitatively significant datasets, which are rarely publicly available. To this end, in this paper we describe a novel experimental framework, that we call the Mouseworld, that combines NFV and SDN to create an environment able to (1) blend and transmit real and synthetic traffic and (2) collect and label this traffic in order to be utilised for training and validating ML algorithms that will be applied to the detection of cybersecurity threats. The Mouseworld framework includes a set of traffic generation, collection and labelling modules, jointly with analytics and algorithm training and visualization components. The OSM open-source network orchestrator is utilized to control and manage the framework and to deploy the training and validation scenarios. We present a preliminary result on the area of Security threat detection as a demonstration of the framework viability.