Date of Award

12-2024

Degree Type

Masters Thesis

Degree Name

Master of Science (MS)

School

Computing Sciences and Computer Engineering

Committee Chair

Dr. Nick Rahimi

Committee Chair School

Computing Sciences and Computer Engineering

Committee Member 2

Dr. Andrew H Sung

Committee Member 2 School

Computing Sciences and Computer Engineering

Committee Member 3

Dr. Partha Sengupta

Committee Member 3 School

Computing Sciences and Computer Engineering

Committee Member 4

Dr. Zhaoxian Zhou

Committee Member 4 School

Computing Sciences and Computer Engineering

Abstract

In the current digital era, cybersecurity has emerged as a major responsibility for companies everywhere. Due to more sophisticated cyber-attacks, IT systems are becoming more complicated. Thus, the effective vulnerability management solutions are becoming more and more important. Prioritizing risks is important since it helps businesses allocate resources and deal with the most serious security concerns. An overview of vulnerability prioritizing techniques is provided in this document, with a focus on the importance of precisely assessing and ranking vulnerabilities according to their base score and the title of the risk. A formula has been proposed by assigning weights for the base score and the title. By taking a variety of base-to-title ratios, we achieved accurate results for 7:3 ratio. By using this ratio, we prioritized the threats and classified them based on achieved priority score. The classification task is done for the self-prepared dataset in which we used five different algorithms. It includes, SVM, Naïve Bayes, Neural Network, XG Boost, Gradient Boosting. Out of all, XG Boost algorithm performed well with an accuracy of 96.7 percent. By using this approach, organizations can rank their threats and allocate them to the resources effectively.

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Available for download on Thursday, December 31, 2026

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