Towards Better Understanding of Cybercrime: The Role of Fine-Tuned LLMs in Translation

arXiv:2404.01940v1 Announce Type: new
Abstract: 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. …

abstract arxiv communications cs.cl cybercrime cybersecurity defence english human intelligence llms machine machine translation processing role translation type understanding