Work

"Exploring the Training Data Landscape for AI Based Threathunting For Protecting Intellectual Property"

Year: 2025

Type: article

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.

Source: ODU Digital Commons (Old Dominion University)

DOI: 10.25776/e1h6-ye98

Authors: Siibo, Manzi, Kreider, Christopher

Institution: Christopher Newport University

Language: English

Topic: Network Security and Intrusion Detection

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6908480849

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