Honors Theses

Date of Award


Document Type

Undergraduate Thesis

Degree Name



Computer Science

Faculty Mentor

Dr. J. Todd McDonald


Dr. Ryan Benton, Dr. Michael Black


Cyberattacks are increasing in size and scope yearly, and the most effective and common means of attack is through malicious software executed on target devices of interest. Malware threats vary widely in terms of behavior and impact and, thus, effective methods of detection are constantly being sought from the academic research community to offset both volume and complexity. Rootkits are malware that represent a highly feared threat because they can change operating system integrity and alter otherwise normally functioning software. Although normal methods of detection that are based on signatures of known malware code are the standard line of defense, rootkits that have never been seen before (zero-day threats) are not easily defeated because of their ability to evade scanners and present false system information. In this research, we propose to evaluate a novel approach of rootkit detection based on collection of time-serial voltage data from the internal motherboard of standard desktop PCs.