CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866913346600566784 |
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| author | Patel, Urjitkumar Yeh, Fang-Chun Gondhalekar, Chinmay |
| author_facet | Patel, Urjitkumar Yeh, Fang-Chun Gondhalekar, Chinmay |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06772 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM Patel, Urjitkumar Yeh, Fang-Chun Gondhalekar, Chinmay Cryptography and Security Artificial Intelligence Computation and Language 68T50, 68T07 (Primary) 03B65, 91F20 (Secondary) I.2.7; I.2.1; I.5.1; I.5.2; I.5.4; H.3.3 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. |
| title | CANAL -- Cyber Activity News Alerting Language Model: Empirical Approach vs. Expensive LLM |
| topic | Cryptography and Security Artificial Intelligence Computation and Language 68T50, 68T07 (Primary) 03B65, 91F20 (Secondary) I.2.7; I.2.1; I.5.1; I.5.2; I.5.4; H.3.3 |
| url | https://arxiv.org/abs/2405.06772 |