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

Modeling Coherency in Generated Emails by Leveraging Deep Neural Learners