Web Scale Graph Mining for Cyber Threat Intelligence
Scott Freitas, Amir Gharib

TL;DR
TITAN is a scalable, real-time graph mining framework integrated into Microsoft USOP that enhances cyber threat intelligence, detection, and disruption capabilities across large-scale networks with high accuracy and efficiency.
Contribution
The paper introduces TITAN, a novel industry-scale graph mining system that dynamically models, updates, and propagates threat intelligence at unprecedented speed and scale.
Findings
Achieved an average macro-F1 score of 0.89 in threat detection.
Enabled a 6x increase in non-file threat intelligence.
Increased incident disruption rate by 21%, reduced disruption time by 1.9x.
Abstract
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millions of entities, incidents, and organizations; (2) real-time update mechanisms that automatically decay and prune outdated intel; (3) integration of security domain knowledge to bootstrap initial reputation scores; and (4)…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsSpam and Phishing Detection · Web Data Mining and Analysis · Complex Network Analysis Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
