Automated email Generation for Targeted Attacks using Natural Language

Understand

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 \textit{fake} emails with malicious content, customized depending on the attacker's intent.