  Generating user activity is a key capability for both evaluating security
monitoring tools as well as improving the credibility of attacker analysis
platforms (e.g., honeynets). In this paper, to generate this activity, we
instrument each machine by means of an external agent. This agent combines both
deterministic and deep learning based methods to adapt to different environment
(e.g., multiple OS, software versions, etc.), while maintaining high
performances. We also propose conditional text generation models to facilitate
the creation of conversations and documents to accelerate the definition of
coherent, system-wide, life scenarios.
