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PERCEIVE: Proactive Exploration of Risky Concept Emergence for Identifying Vulnerabilities & Exposures

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Title:PERCEIVE: Proactive Exploration of Risky Concept Emergence for Identifying Vulnerabilities & Exposures
Authors:Paradis, Carlos Vinicius
Contributors:Kazman, Rick (advisor)
Computer Science (department)
Keywords:Computer science
Artificial intelligence
Aerospace engineering
show 4 moreNLP
safety threat
software vulnerability
topic modeling
show less
Date Issued:2021
Publisher:University of Hawai'i at Manoa
Abstract:National databases that collect various kinds of textual threat reports such as ASRS, CERT, and NVD manually process their reports individually. They then offer data products to disseminate the aggregate information, like newsletters, alerts or individual report searching. The goal of this research is to connect these individual reports thematically and temporally to identify emerging or recurring threats, by analyzing large collections of text, source code, collaboration and communication patterns.
This capability, I argue, enables us to identify the emergence and recurrence of such themes, and the contexts in which they re-occur, facilitating faster and more capable mitigation. I propose two models to shed light on this goal: An empirical model of vulnerabilities as bugs, the commit flow model, and one of the vulnerabilities and aviation safety threats as topics, the topic flow model. I use as gold standard existing manual workflows in both domains, reflected in the existing data products by these organizations, and empirically evaluate if the automated models can match or outperform existing manual practices.
Pages/Duration:145 pages
Rights:All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
Appears in Collections: Ph.D. - Computer Science

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