mlsanityData to 7 Oct 2026

Web Scale Graph Mining for Cyber Threat Intelligence

arXiv 2411.06239 ↗First posted 9 Nov 2024cs.CR cs.LG cs.SIcited by 0

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Abstract (arXiv)

Defending against today's increasingly sophisticated and large-scale cyberattacks demands accurate, real-time threat intelligence. Traditional approaches struggle to scale, integrate diverse telemetry, and adapt to a constantly evolving security landscape. We introduce Threat Intelligence Tracking via Adaptive Networks (TITAN), an industry-scale graph mining framework that generates cyber threat intelligence at unprecedented speed and scale. TITAN introduces a suite of innovations specifically designed to address the complexities of the modern security landscape, including: (1) a dynamic threat intelligence graph that maps the intricate relationships between millions of entities, incidents, and organizations; (2) real-time update mechanisms that automatically decay and prune outdated intel; (3) integration of security domain knowledge to bootstrap initial reputation scores; and (4) reputation propagation algorithms that uncover hidden threat actor infrastructure. Integrated into Microsoft Unified Security Operations Platform (USOP), which is deployed across hundreds of thousands of organizations worldwide, TITAN's threat intelligence powers key detection and disruption capabilities. With an impressive average macro-F1 score of 0.89 and a precision-recall AUC of 0.94, TITAN identifies millions of high-risk entities each week, enabling a 6x increase in non-file threat intelligence. Since its deployment, TITAN has increased the product's incident disruption rate by a remarkable 21%, while reducing the time to disrupt by a factor of 1.9x, and maintaining 99% precision, as confirmed by customer feedback and thorough manual evaluation by security experts--ultimately saving customers from costly security breaches.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 65 references that are in the map. First posted on arXiv 9 Nov 2024.

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17 of 65 references.

References · 17

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  3. 2013Loopy Belief Propagation for Approximate Inference: An Empirical Studycited by 76
  4. 2020A Large-Scale Database for Graph Representation Learningcited by 54
  5. 2022What Makes Graph Neural Networks Miscalibrated?cited by 39
  6. 2021AttacKG: Constructing Technique Knowledge Graph from Cyber Threat Intelligence Reportscited by 31
  7. 2014Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Datacited by 13
  8. 2020Enabling Efficient Cyber Threat Hunting With Cyber Threat Intelligencecited by 12
  9. 2021MalNet: A Large-Scale Image Database of Malicious Softwarecited by 11
  10. 2022On Calibration of Graph Neural Networks for Node Classificationcited by 9
  11. 2019Cyber-All-Intel: An AI for Security related Threat Intelligencecited by 7
  12. 2020REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wildcited by 7
5 more
  1. 2024AI-Driven Guided Response for Security Operation Centers with Microsoft Copilot for Securitycited by 7
  2. 2022A review of knowledge graph application scenarios in cyber securitycited by 6
  3. 2022Phishing URL Detection: A Network-based Approach Robust to Evasioncited by 6
  4. 2024GraphWeaver: Billion-Scale Cybersecurity Incident Correlationcited by 3
  5. 2021Malware Knowledge Graph Generationcited by 1

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