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Date of Award
Dissertation - NSU Access Only
Doctor of Philosophy in Information Systems (DISS)
Graduate School of Computer and Information Sciences
As U.S federal government agencies have increased the use of the Internet to utilize technologies such as e-learning, U.S. federal government information systems have become more exposed to security vulnerabilities that may contribute to system attacks and system exploitation. U.S. federal government agencies are required to come up with their own security solutions for ensuring their information systems are secured, however, security experts are having difficulties identifying what is needed to classify their information systems as secured.
The aim of this developmental study is to develop an audit classification index (ACI) to assist in identifying vulnerabilities and classifying electronic learning (e-learning) systems at U.S. federal government agencies. The study identified the requirements for performing an audit of e-learning systems in U.S. federal government agencies. After the requirements were identified, the study used the ACI to audit the federal e-learning systems using a black-box approach and classified the e-learning systems based on the results of the audit. Additionally, a comparative group of electronic government (e-government) systems were also audited and classified using the ACI to compare the results against the e-learning systems.
This study sought to contribute to the body of knowledge regarding the information security of U.S. federal e-learning systems by developing an ACI that can be used to identify vulnerabilities and classify U.S. federal e-learning systems as secured, good, marginal, unsatisfactory, or unsecured. By identifying the vulnerabilities of a particular information system, security experts should have a better understanding of what is needed to secure and determine the security level of U.S. federal information systems.
Gerald Deawne Johnson. 2012. Development of an Audit Classification Index (ACI) for Federal e-learning Systems Security Vulnerabilities. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, Graduate School of Computer and Information Sciences. (187)