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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00380664" target="_blank" >RIV/68407700:21230/24:00380664 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1109/EuroSPW61312.2024.00017" target="_blank" >https://doi.org/10.1109/EuroSPW61312.2024.00017</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EuroSPW61312.2024.00017" target="_blank" >10.1109/EuroSPW61312.2024.00017</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/VJ02010020" target="_blank" >VJ02010020: AI-Dojo: Multiagentní testbed pro výzkum a testování umělé inteligence v kyberbezpečnosti</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings - 9th IEEE European Symposium on Security and Privacy Workshops, Euro S and PW 2024

ISBN

979-8-3503-6729-4

ISSN

2768-0649

e-ISSN

2768-0657

Počet stran výsledku

9

Strana od-do

91-99

Název nakladatele

IEEE Computer Society

Místo vydání

Cannes

Místo konání akce

Vídeň

Datum konání akce

8. 7. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001302657400011