SecureBERT: A Domain-Specific Language Model for Cybersecurity

Aghaei, Ehsan; Niu, Xi; Shadid, Waseem; Al-Shaer, Ehab

doi:10.1007/978-3-031-25538-0_3

Cited by 15 publications

(1 citation statement)

References 21 publications

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“…This approach demands a profound knowledge of the mitre ATT&CK framework and the specific techniques that adversaries use to exploit the vulnerability and affect the system . Automated mapping uses various techniques, such as natural language processing and machine learning, to analyze the details of a CVE and identify the corresponding mitre ATT&CK techniques and tactics Baccar et al (2021); Ampel et al (2023); Abdeen et al (2023); Aghaei et al (2023). BRON is a bi-directional aggregated data graph that connects various cybersecurity knowledge sources.…”

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confidence: 99%

Mapping Vulnerability Description to MITRE ATT&CK Framework by LLM

Rafiey,

Namadchian

2024

Preprint

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As the number and complexity of cybersecurity threats continue to increase, security professionals must augment their knowledge by utilizing resources that provide insights into the attack patterns and techniques employed by attackers. This understanding allows them to better comprehend the potential impact of a vulnerability and prioritize the development of effective mitigation strategies within their organizations. Given the frequent generation of CVEs and the impossibility of manually mapping them to MITRE ATT&CK techniques, relying on automation methods such as BERT, a language model requiring training and fine-tuning becomes both expensive and time-consuming. To address this issue, our paper proposes a cost-effective approach using a general-purpose chatbot like GPT-3 to perform CVE to ATT&CK mapping, which yields similar results with lower costs and greater expandability.

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confidence: 99%