Cyber-All-Intel: An AI for Security related Threat Intelligence

Author/Creator ORCID

Date

2019-05-07

Department

Program

Citation of Original Publication

Sudip Mittal, Anupam Joshi, Tim Finin, Cyber-All-Intel: An AI for Security related Threat Intelligence, Computer Science, Artificial Intelligence, 2019, https://arxiv.org/abs/1905.02895

Rights

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Abstract

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 paper 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 pro-actively 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.