Knowledge for Cyber Threat Intelligence

Keeping up with threat intelligence is a must for a security analyst today. There is a volume of information present in 'the wild' that affects an organization. We need to develop an artificial intelligence system that scours the intelligence sources, to keep the analyst updated about various threats that pose a risk to her organization. A security analyst who is better 'tapped in' can be more effective.

In this thesis, we present, Cyber-All-Intel an artificial intelligence system to aid a security analyst. It is a system for knowledge extraction, representation, and analytics in an end-to-end pipeline grounded in the cybersecurity informatics domain. It uses multiple knowledge representations like vector spaces and knowledge graphs in a `VKG structure' to store incoming intelligence. The system also uses neural network models to proactively improve its knowledge. We have also created a query engine and an alert system that can be used by an analyst to find actionable cybersecurity insights

cybersecurity, graph embedding, knowledge graphs


University of Maryland, Baltimore County

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