Uncovering and understanding the intent of an unknown piece of software is a significant task for cyber security professionals. Software reverse engineers face a formidable cognitive load when analyzing unknown binary artifacts for security vulnerabilities. Substantial benefits both for the training and productivity of binary analysis work can be realized once the cognitively challenging tasks are identified and categorized. This paper begins the development of a taxonomy of various software reverse engineering tasks and their cognitive challenges within cyber security. As a result of this research complex software reverse engineering tasks can be improved through future automation and human-computer interface advancements.