Using Evolutionary Algorithms to Identify Problematic Parameter Settings in Software Configurations
Electronic Theses and Dissertations
Item Files
Item Details
- abstract
- As software systems become more complex and configurable, failures due to misconfigurations are becoming more common. Many cyber attacks can be attributed to administrators who, unaware of insecure settings or novel attacks, expose vulnerabilities in their systems. The difficulty of diagnosing and fixing misconfigurations is primarily due to the large number of possible configuration parameter settings and the potential existence of interdependencies between them.
- subject
- Configuration Management
- Genetic Algorithms
- Parameter chains
- secure configurations
- contributor
- Fulp, Errin W (committee chair)
- Cañas, Daniel A (committee member)
- Gage, Howard D (committee member)
- date
- 2017-06-15T08:36:04Z (accessioned)
- 2017-06-15T08:36:04Z (available)
- 2017 (issued)
- degree
- Computer Science (discipline)
- identifier
- http://hdl.handle.net/10339/82218 (uri)
- language
- en (iso)
- publisher
- Wake Forest University
- title
- Using Evolutionary Algorithms to Identify Problematic Parameter Settings in Software Configurations
- type
- Thesis