text: With an increasing number of malicious attacks, the number of people and organizations falling prey to social engineering attacks is proliferating. Despite considerable research in mitigation systems, attackers continually improve their modus operandi by using sophisticated machine learning, natural language processing techniques with an intent to launch successful targeted attacks aimed at deceiving detection mechanisms as well as the victims. We propose a system for advanced email masquerading attacks using Natural Language Generation (NLG) techniques. Using legitimate as well as an influx of varying malicious content, the proposed deep learning system generates emails with malicious content, customized depending on the attacker's intent. The system leverages Recurrent Neural Networks (RNNs) for automated text generation. We also focus on the performance of the generated emails in defeating statistical detectors, and compare and analyze the emails using a proposed baseline.
  @cite_9: This paper describes a two-stage process for stochastic generation of email, in which the first stage structures the emails according to sender style and topic structure (high-level generation), and the second stage synthesizes text content based on the particulars of an email element and the goals of a given communication (surface-level realization). Synthesized emails were rated in a preliminary experiment. The results indicate that sender style can be detected. In addition we found that stochastic generation performs better if applied at the word level than at an original-sentence level (“template-based”) in terms of email coherence, sentence fluency, naturalness, and preference.
  @cite_14: This paper presents the design and implementation details of an email synthesizer using two-stage stochastic natural language generation, where the first stage structures the emails according to sender style and topic structure, and the second stage synthesizes text content based on the particulars of an email structure element and the goals of a given communication for surface realization. The synthesized emails reflect sender style and the intent of communication, which can be further used as synthetic evidence for developing other applications.
summary: The system used for synthesizing emails in this work is somewhat aligned along the lines of the methodology described in @cite_14 @cite_9 . However, our proposed system has no manual labor involved and with some level of post processing has been shown to deceive an automated supervised classification system.