Abstract
Cyber threat intelligence powers machine learning and other data analysis techniques that provide enhanced protection and situational awareness to critical infrastructure application spaces. The Geo Threat Observable (GTO) research project gathers, processes, manages, and analyzes cyber threat intelligence to provide predictions based on system data, recover missing links in threat intelligence, and enable better use of limited cyber defense resources. Especially focused on the energy sector, GTO provides coherent common operational descriptions of infrastructure landscapes to empower cyber threat defenders. This paper focuses on the aggregation, cleaning, processing, management, validation, and analysis of cyber threat intelligence for the GTO project.
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CITATION STYLE
Wolf, S., Foster, R., Mack, A., Priest, Z., & Haile, J. (2022). Data Collection and Exploratory Analysis for Cyber Threat Intelligence Machine Learning Processes. In Proceedings - 2022 9th Swiss Conference on Data Science, SDS 2022 (pp. 7–12). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/SDS54800.2022.00009
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