Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Williams, Byron J.

Committee Member

Dampier, David A.

Committee Member

Bradshaw, Gary

Committee Member

McGrew, Robert Wesley

Date of Degree

8-12-2016

Document Type

Dissertation - Open Access

Major

Computer Science

Degree Name

Doctor of Philosophy

College

James Worth Bagley College of Engineering

Department

Department of Computer Science and Engineering

Abstract

Vulnerable code may cause security breaches in software systems resulting in financial and reputation losses for the organizations in addition to loss of their customers’ confidential data. Delivering proper software security training to software developers is key to prevent such breaches. Conventional training methods do not take the code written by the developers over time into account, which makes these training sessions less effective. We propose a method for recommending computer–security training to help identify focused and narrow areas in which developers need training. The proposed method leverages the power of static analysis techniques, by using the flagged vulnerabilities in the source code as basis, to suggest the most appropriate training topics to different software developers. Moreover, it utilizes public vulnerability repositories as its knowledgebase to suggest community accepted solutions to different security problems. Such mitigation strategies are platform independent, giving further strength to the utility of the system. This research discussed the proposed architecture of the recommender system, case studies to validate the system architecture, tailored algorithms to improve the performance of the system, and human subject evaluation conducted to determine the usefulness of the system. Our evaluation suggests that the proposed system successfully retrieves relevant training articles from the public vulnerability repository. The human subjects found these articles to be suitable for training. The human subjects also found the proposed recommender system as effective as a commercial tool.

URI

https://hdl.handle.net/11668/20000

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