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.

我们的研究提出了一个新的经验性框架，利用 BERT 模型解析和分类新闻文章中的网络相关信息，提高市场利益相关者的实时警惕性。该框架通过使用基于随机森林的新型银标签方法，将其命名为 CANAL（网络活动新闻警报语言模型），以进行网络分类。我们将 CANAL 与更大、更昂贵的 LLMs 进行了对比，并突出了它们在网络新闻分类中的零到少训练的学习能力。CANAL 在准确性和成本效益方面表现出卓越的性能。此外，我们引入了网络信号发现模块，这是一个高效检测新闻文章中新兴网络信号的战略组件。综合而言，CANAL 和网络信号发现模块使我们的框架能够为需要快速响应网络情报的企业提供稳健且经济高效的解决方案。