GraphWeaver: Billion-Scale Cybersecurity Incident Correlation
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913376363347968 |
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| author | Freitas, Scott Gharib, Amir |
| author_facet | Freitas, Scott Gharib, Amir |
| contents | In the dynamic landscape of large enterprise cybersecurity, accurately and efficiently correlating billions of security alerts into comprehensive incidents is a substantial challenge. Traditional correlation techniques often struggle with maintenance, scaling, and adapting to emerging threats and novel sources of telemetry. We introduce GraphWeaver, an industry-scale framework that shifts the traditional incident correlation process to a data-optimized, geo-distributed graph based approach. GraphWeaver introduces a suite of innovations tailored to handle the complexities of correlating billions of shared evidence alerts across hundreds of thousands of enterprises. Key among these innovations are a geo-distributed database and PySpark analytics engine for large-scale data processing, a minimum spanning tree algorithm to optimize correlation storage, integration of security domain knowledge and threat intelligence, and a human-in-the-loop feedback system to continuously refine key correlation processes and parameters. GraphWeaver is integrated into the Microsoft Defender XDR product and deployed worldwide, handling billions of correlations with a 99% accuracy rate, as confirmed by customer feedback and extensive investigations by security experts. This integration has not only maintained high correlation accuracy but reduces traditional correlation storage requirements by 7.4x. We provide an in-depth overview of the key design and operational features of GraphWeaver, setting a precedent as the first cybersecurity company to openly discuss these critical capabilities at this level of depth. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01842 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GraphWeaver: Billion-Scale Cybersecurity Incident Correlation Freitas, Scott Gharib, Amir Cryptography and Security Social and Information Networks In the dynamic landscape of large enterprise cybersecurity, accurately and efficiently correlating billions of security alerts into comprehensive incidents is a substantial challenge. Traditional correlation techniques often struggle with maintenance, scaling, and adapting to emerging threats and novel sources of telemetry. We introduce GraphWeaver, an industry-scale framework that shifts the traditional incident correlation process to a data-optimized, geo-distributed graph based approach. GraphWeaver introduces a suite of innovations tailored to handle the complexities of correlating billions of shared evidence alerts across hundreds of thousands of enterprises. Key among these innovations are a geo-distributed database and PySpark analytics engine for large-scale data processing, a minimum spanning tree algorithm to optimize correlation storage, integration of security domain knowledge and threat intelligence, and a human-in-the-loop feedback system to continuously refine key correlation processes and parameters. GraphWeaver is integrated into the Microsoft Defender XDR product and deployed worldwide, handling billions of correlations with a 99% accuracy rate, as confirmed by customer feedback and extensive investigations by security experts. This integration has not only maintained high correlation accuracy but reduces traditional correlation storage requirements by 7.4x. We provide an in-depth overview of the key design and operational features of GraphWeaver, setting a precedent as the first cybersecurity company to openly discuss these critical capabilities at this level of depth. |
| title | GraphWeaver: Billion-Scale Cybersecurity Incident Correlation |
| topic | Cryptography and Security Social and Information Networks |
| url | https://arxiv.org/abs/2406.01842 |