AI-Driven Cyber Threat Intelligence Automation
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866913565453058048 |
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| author | Shah, Shrit Parast, Fatemeh Khoda |
| author_facet | Shah, Shrit Parast, Fatemeh Khoda |
| contents | This study introduces an innovative approach to automating Cyber Threat Intelligence (CTI) processes in industrial environments by leveraging Microsoft's AI-powered security technologies. Historically, CTI has heavily relied on manual methods for collecting, analyzing, and interpreting data from various sources such as threat feeds. This study introduces an innovative approach to automating CTI processes in industrial environments by leveraging Microsoft's AI-powered security technologies. Historically, CTI has heavily relied on manual methods for collecting, analyzing, and interpreting data from various sources such as threat feeds, security logs, and dark web forums -- a process prone to inefficiencies, especially when rapid information dissemination is critical. By employing the capabilities of GPT-4o and advanced one-shot fine-tuning techniques for large language models, our research delivers a novel CTI automation solution. The outcome of the proposed architecture is a reduction in manual effort while maintaining precision in generating final CTI reports. This research highlights the transformative potential of AI-driven technologies to enhance both the speed and accuracy of CTI and reduce expert demands, offering a vital advantage in today's dynamic threat landscape. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20287 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AI-Driven Cyber Threat Intelligence Automation Shah, Shrit Parast, Fatemeh Khoda Cryptography and Security Artificial Intelligence Computers and Society This study introduces an innovative approach to automating Cyber Threat Intelligence (CTI) processes in industrial environments by leveraging Microsoft's AI-powered security technologies. Historically, CTI has heavily relied on manual methods for collecting, analyzing, and interpreting data from various sources such as threat feeds. This study introduces an innovative approach to automating CTI processes in industrial environments by leveraging Microsoft's AI-powered security technologies. Historically, CTI has heavily relied on manual methods for collecting, analyzing, and interpreting data from various sources such as threat feeds, security logs, and dark web forums -- a process prone to inefficiencies, especially when rapid information dissemination is critical. By employing the capabilities of GPT-4o and advanced one-shot fine-tuning techniques for large language models, our research delivers a novel CTI automation solution. The outcome of the proposed architecture is a reduction in manual effort while maintaining precision in generating final CTI reports. This research highlights the transformative potential of AI-driven technologies to enhance both the speed and accuracy of CTI and reduce expert demands, offering a vital advantage in today's dynamic threat landscape. |
| title | AI-Driven Cyber Threat Intelligence Automation |
| topic | Cryptography and Security Artificial Intelligence Computers and Society |
| url | https://arxiv.org/abs/2410.20287 |