Abstract
This study provides a comprehensive evaluation of the effectiveness that would result in the integration of AI into traditional threat hunting systems. To do so, 10-15 scholarly articles and data sets were evaluated to see the results of AI and machine learning threat hunting versus traditional systems. With so many proven benefits of this integration, this paper also explores how it impacts the protection of Intellectual property which is some of the most important forms of information that threat hunting systems aim to protect.
Faculty Advisor/Mentor
Christopher Kreider
Document Type
Paper
Disciplines
Artificial Intelligence and Robotics | Cybersecurity | Information Security
DOI
10.25776/e1h6-ye98
Publication Date
4-25-2025
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Included in
Artificial Intelligence and Robotics Commons, Cybersecurity Commons, Information Security Commons
"Exploring the Training Data Landscape for AI Based Threathunting For Protecting Intellectual Property"
This study provides a comprehensive evaluation of the effectiveness that would result in the integration of AI into traditional threat hunting systems. To do so, 10-15 scholarly articles and data sets were evaluated to see the results of AI and machine learning threat hunting versus traditional systems. With so many proven benefits of this integration, this paper also explores how it impacts the protection of Intellectual property which is some of the most important forms of information that threat hunting systems aim to protect.