  Cyber deception is emerging as a promising approach to defending networks and
systems against attackers and data thieves. However, despite being relatively
cheap to deploy, the generation of realistic content at scale is very costly,
due to the fact that rich, interactive deceptive technologies are largely
hand-crafted. With recent improvements in Machine Learning, we now have the
opportunity to bring scale and automation to the creation of realistic and
enticing simulated content. In this work, we propose a framework to automate
the generation of email and instant messaging-style group communications at
scale. Such messaging platforms within organisations contain a lot of valuable
information inside private communications and document attachments, making them
an enticing target for an adversary. We address two key aspects of simulating
this type of system: modelling when and with whom participants communicate, and
generating topical, multi-party text to populate simulated conversation
threads. We present the LogNormMix-Net Temporal Point Process as an approach to
the first of these, building upon the intensity-free modeling approach of
Shchur et al. to create a generative model for unicast and multi-cast
communications. We demonstrate the use of fine-tuned, pre-trained language
models to generate convincing multi-party conversation threads. A live email
server is simulated by uniting our LogNormMix-Net TPP (to generate the
communication timestamp, sender and recipients) with the language model, which
generates the contents of the multi-party email threads. We evaluate the
generated content with respect to a number of realism-based properties, that
encourage a model to learn to generate content that will engage the attention
of an adversary to achieve a deception outcome.
