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Research

Attack Path Modeling Research

This paper outlines the Attack Path modeling (APM) module of Darktrace PREVENT, exploring how real-time, automated, dual-aspect, multi-data-source, end-to-end APM can be used to give blue teams a comprehensive view of realistic, risk-prioritized attack paths so that resources can be best allocated to defend key assets.

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Research Abstracts
Rapid Process-Chain Anomaly Detection Using a Multistage Classifier
Sorting long lists of file names by relevance and sensitive content
A system for analyzing network activity to detect stealthy cryptocurrency mining
Autonomous detection of the intended function of a corporate inbox through meta-scoring
Using epidemiology theory to identify the most damaging network devices
Automatic Identification of Scanned IP Ranges
A real-time, self-correcting similarity classifier for emails
Using graph theory to identify critical nodes within computer networks
Analyzing network activity to detect compromised devices sending spam emails
Detecting and preventing misdirected emails with correspondence semantics

Backed in Research.

About the AI Research Centre

In existence since Darktrace’s inception in 2013, the Darktrace AI Research Centre is foundational to our continued innovation. Rather than a defined product roadmap, the Centre looks at how AI can be applied to real-world challenges, to find solutions that cannot be achieved by humans alone.
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