Dissertation/Thesis Abstract

Using Code Inspection, Code Modification, and Machine Learning to prevent SQL Injection
by Trumble, Brandon, M.S., Kutztown University of Pennsylvania, 2015, 50; 1590429
Abstract (Summary)

Modern day databases store invaluable information about everyone. This information is assumed to be safe, secure, and confidential. However, as technology has become more widespread, more people are able to abuse and exploit this information for personal gain. While the ideal method to combat this issue is the enhanced education of developers, that still leaves a large amount of time where this information is insecure. This thesis outlines two potential solutions to the problem that SQL Injection presents in the context of databases. The first modifies an existing code base to use safe prepared statements rather than unsafe standard queries. The second is a neural network application that sits between the user-facing part of a web application and the application itself. The neural network is designed to analyze data being submitted by a user and detect attempts at SQL injection.

Indexing (document details)
Advisor: Kaplan, Randy M.
Commitee: Frye, Lisa, Rieksts, Oskars
School: Kutztown University of Pennsylvania
Department: Computer and Information Science
School Location: United States -- Pennsylvania
Source: MAI 54/05M(E), Masters Abstracts International
Subjects: Computer science
Keywords: Information security, Machine learning, Neural networks, Prepared statements, Sql injection
Publication Number: 1590429
ISBN: 9781321798319
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