With an increasing number of malicious attacks, the number of people and organizations falling prey to social engineering attacks are 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 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.
@InProceedings{DAS18.4, author = {Avisha Das and Rakesh Verma}, title = {Automated email Generation for Targeted Attacks using Natural Language}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Els Lefever and Bart Desmet and Guy De Pauw}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-27-6}, language = {english} }
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