CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs Expensive LLM
<|reference_start|>CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs Expensive LLM: 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.<|reference_end|>
@article{patel2024canal,
    title={CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach
  vs. Expensive LLM},
    author={Urjitkumar Patel, Fang-Chun Yeh, Chinmay Gondhalekar},
    journal={2024 IEEE 3rd International Conference on AI in Cybersecurity
  (ICAIC), Houston, TX, USA, 2024, pp. 1-12},
    year={2024},
    doi={10.1109/ICAIC60265.2024.10433839},
    archivePrefix={arXiv},
    eprint={2405.06772},
    primaryClass={cs.CR cs.AI cs.CL}
}