Publication Date
Spring 2019
Degree Type
Master's Project
Degree Name
Master of Science (MS)
Department
Computer Science
First Advisor
Mark Stamp
Second Advisor
Thomas Austin
Third Advisor
Fabio Di Troia
Keywords
machine learning, deep learning, malware detection
Abstract
It is often claimed that the primary advantage of deep learning is that such models can continue to learn as more data is available, provided that sufficient computing power is available for training. In contrast, for other forms of machine learning it is claimed that models ‘‘saturate,’’ in the sense that no additional learning can occur beyond some point, regardless of the amount of data or computing power available. In this research, we compare the accuracy of deep learning to other forms of machine learning for malware detection, as a function of the training dataset size. We experiment with a wide variety of hyperparameters for our deep learning models, and we compare these models to results obtained using �-nearest neighbors. In these experiments, we use a subset of a large and diverse malware dataset that was collected as part of a recent research project.
Recommended Citation
Jain, Parth, "Machine Learning versus Deep Learning for Malware Detection" (2019). Master's Projects. 704.
DOI: https://doi.org/10.31979/etd.56y7-b74e
https://scholarworks.sjsu.edu/etd_projects/704
