Towards Better Understanding of Cybercrime: The Role of Fine-Tuned LLMs
  in Translation
Veronica Valeros Anna Širokova Carlos Catania Sebastian Garcia
Understanding cybercrime communications is paramount for cybersecurity
defence. This often involves translating communications into English for
processing, interpreting, and generating timely intelligence. The problem is
that translation is hard. Human translation is slow, expensive, and scarce.
Machine translation is inaccurate and biased. We propose using fine-tuned Large
Language Models (LLM) to generate translations that can accurately capture the
nuances of cybercrime language. We apply our technique to public chats from the
NoName057(16) Russian-speaking hacktivist group. Our results show that our
fine-tuned LLM model is better, faster, more accurate, and able to capture
nuances of the language. Our method shows it is possible to achieve
high-fidelity translations and significantly reduce costs by a factor ranging
from 430 to 23,000 compared to a human translator.