Automated email Generation for Targeted Attacks using Natural Language
This addresses the challenge of improving social engineering attacks for malicious actors, but it is incremental as it applies existing NLG methods to a specific domain.
The paper tackles the problem of generating deceptive emails for targeted attacks using natural language generation, resulting in a system that produces fake emails with malicious content that can evade statistical detectors.
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. 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.