Date Approved
12-15-2022
Graduate Degree Type
Project
Degree Name
Computer Information Systems (M.S.)
Degree Program
School of Computing and Information Systems
First Advisor
Xinli Wang
Academic Year
2022/2023
Abstract
Malicious software poses a serious threat to the cybersecurity of network infrastructures and is a global pandemic in the form of computer viruses, Trojan horses, and Internet worms. Studies imply that the effects of malware are deteriorating. The main defense against malware is malware detectors. The methods that such a detector employ define its level of quality. Therefore, it is crucial that we research malware detection methods and comprehend their advantages and disadvantages. Attackers are creating malware that is polymorphic and metamorphic and has the capacity to modify their source code as they spread. Furthermore, existing defenses, which often utilize signature-based approaches and are unable to identify the previously undiscovered harmful executables, are significantly undermined by the diversity and volume of their variations. Malware families' variations exhibit common behavioral characteristics that reveal their origin and function. Machine learning techniques may be used to detect and categorize novel viruses into their recognized families utilizing the behavioral patterns discovered via static or dynamic analysis. In this paper, we'll talk about malware, its various forms, malware concealment strategies, and malware attack mechanisms. Additionally, many detection methods and classification models are presented in this study. The method of malware analysis is demonstrated by conducting an analysis of a malware program in a contained environment.
ScholarWorks Citation
Suraneni, Namratha, "Malware Detection and Analysis" (2022). Culminating Experience Projects. 227.
https://scholarworks.gvsu.edu/gradprojects/227
