CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM
| Autor: | Patel, Urjitkumar, Yeh, Fang-Chun, Gondhalekar, Chinmay |
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| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | 2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC), Houston, TX, USA, 2024, pp. 1-12 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.1109/ICAIC60265.2024.10433839 |
| Popis: | In today's digital landscape, where cyber attacks have become the norm, the detection of cyber attacks and threats is critically imperative across diverse domains. Our research presents a new empirical framework for cyber threat modeling, adept at parsing and categorizing cyber-related information from news articles, enhancing real-time vigilance for market stakeholders. At the core of this framework is a fine-tuned BERT model, which we call CANAL - Cyber Activity News Alerting Language Model, tailored for cyber categorization using a novel silver labeling approach powered by Random Forest. We benchmark CANAL against larger, costlier LLMs, including GPT-4, LLaMA, and Zephyr, highlighting their zero to few-shot learning in cyber news classification. CANAL demonstrates superior performance by outperforming all other LLM counterparts in both accuracy and cost-effectiveness. Furthermore, we introduce the Cyber Signal Discovery module, a strategic component designed to efficiently detect emerging cyber signals from news articles. Collectively, CANAL and Cyber Signal Discovery module equip our framework to provide a robust and cost-effective solution for businesses that require agile responses to cyber intelligence. Comment: Published in 2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC), Conference Date: 07-09 February 2024 |
| Databáze: | arXiv |
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