Date of Award
8-16-2022
Document Type
Thesis
Degree Name
Computer Science, MS
First Advisor
Donghoon Kim
Committee Members
Hai Jiang; Hung-Chi Su
Call Number
LD 251 .A566t 2022 D53
Abstract
Android malware is a growing threat, coinciding with the increasing adoption of the Android platform. Malware detection methods used to maintain user privacy and system integrity are increasingly becoming the subject of research. Many new methods studied employ learning algorithms to detect malicious programs. This study investigates the use of byte and opcode frequency features as inputs for tree-based machine learning methods. The algorithm is optimized to reduce overfitting given input hyperparameter combinations and is tuned using cross-validation procedures. Lastly, the study deliberates on possible avenues for future research to gather more concrete evidence for the efficacy and cost-effectiveness of such a system in a productive environment, emphasizing the need for more strenuous testing processes.
Rights Management
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Dickey, Kyler D., "An analysis of Android malware detection using tree learning techniques" (2022). Student Theses and Dissertations. 255.
https://arch.astate.edu/all-etd/255
