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arXiv · 2405.06772

CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM

Urjitkumar Patel·Fang-Chun Yeh·Chinmay Gondhalekar
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Abstract

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.

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Urjitkumar Patel, Fang-Chun Yeh, Chinmay Gondhalekar. 2024-05-10. CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM. https://doi.org/10.1109/icaic60265.2024.10433839

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