  Advanced machine learning and natural language techniques enable attackers to
launch sophisticated and targeted social engineering-based attacks. To counter
the active attacker issue, researchers have since resorted to proactive methods
of detection. Email masquerading using targeted emails to fool the victim is an
advanced attack method. However automatic text generation requires controlling
the context and coherency of the generated content, which has been identified
as an increasingly difficult problem. The method used leverages a hierarchical
deep neural model which uses a learned representation of the sentences in the
input document to generate structured written emails. We demonstrate the
generation of short and targeted text messages using the deep model. The global
coherency of the synthesized text is evaluated using a qualitative study as
well as multiple quantitative measures.
