Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Bhowmik, Tanmay

Committee Member

Iannucci, Stefano

Committee Member

Crumpton, Joseph J.

Committee Member

Jankun-Kelly, T. J.

Committee Member

Keith, Jason M.

Date of Degree

8-9-2019

Original embargo terms

Visible to MSU only for 3 years

Document Type

Graduate Thesis - Open Access

Major

Computer Science

Degree Name

Master of Science

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Department

Department of Computer Science and Engineering

Abstract

Being software security one of the primary concerns in the software engineering community, researchers are coming up with many preemptive approaches which are primarily designed to detect vulnerabilities in the post-implementation stage of the software development life-cycle (SDLC). While they have been shown to be effective in detecting vulnerabilities, the consequences are often expensive. Accommodating changes after detecting a bug or vulnerability in late stages of the SDLC is costly. On that account, in this thesis, we propose a novel framework to provide an additional measure of predicting vulnerabilities at earlier stages of the SDLC. To that end, we leverage state-of-the-art machine learning classification algorithms to predict vulnerabilities for new requirements. We also present a case study on a large open-source-software (OSS) system, Firefox, evaluating the effectiveness of the extended prediction module. The results demonstrate that the framework could be a viable augmentation to the traditional vulnerabilityighting tools.

URI

https://hdl.handle.net/11668/14520

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