Semester of Graduation
Electrical and Computer Engineering
First Major Professor
Master of Science (MS)
This paper discusses malware detection in personal computers. Current malware detection solutions are static. Antiviruses rely on lists of malicious signatures that are then used in file scanning. These antiviruses are also very dependent on the operating system, requiring different solutions for different systems. This paper presents a solution that detects malware based on runtime attributes. It also emphasizes that these attributes are easily accessible and fairly generic meaning that it functions across systems and without specialized information. The attributes are used in a machine learning system that makes it flexible for retraining if necessary, but capable of handling new variants without needing to modify the solution. It can also be run quickly which allows for detection to be achieved before the malware gets too far.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Sgroi, Michael and Jacobson, Doug, "Dynamic and System Agnostic Malware Detection Via Machine Learning" (2018). Creative Components. 10.