The Usage of Machine Learning for Malware Detection: Examining both the techniques and the challenges; Analyzing the Lifecycle of Health Data Collected during COVID-19
Ursini, Nicholas, School of Engineering and Applied Science, University of Virginia
Ku, Tsai-Hsuan, EN-Engineering and Society, University of Virginia
Kwon, Yonghwi, EN-Comp Science Dept, University of Virginia
Feng, Lu, EN-Comp Science Dept, University of Virginia
As a part of the Technical Paper I will be examining the current research being conducted into the potential uses of machine learning algorithms to train software to detect the presence of malware. Machine learning techniques present an opportunity in the field of malware detection due to their deep learning techniques. Due to the transformations of the data these techniques can find patterns in the data that humans cannot identify. Machine learning based systems have only become practical in recent years and their adoption by various industries is slowly growing, so there are still opportunities for growth and development of these technologies. I will look into the current methods being used to create machine learning models used for malware detection. I will also examine the limitations in these systems and the challenges they are currently facing. Finally I will discuss potential new areas to explore for this field.
For the other part of this project I will be examining the full lifecycle of protected health data collected by new digital health techniques introduced during this pandemic. Both the contact tracing apps I want to examine in the technical report as well as telemedicine, which is the ability to get health care services over the internet, have increased in use during this pandemic. With this increase in digital health techniques comes an increase in health data being collected by these techniques. A lot of these techniques have only been created during this pandemic due to the special circumstances and reduced limitations placed on healthcare providers by the government. I will examine the new uses of digital health technologies, focusing specifically on the contact tracing apps, and examine the full lifecycle of the data they collect and examine the relationship and desires of the people collecting the data and the patients who are having their data collected using the SCOT framework. The technical and STS theses are not related.
BS (Bachelor of Science)
Machine learning, Malware Detection, COVID-19, Digital Health data
School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Yonghwi Kwon
STS Advisor: Tsai-Hsuan Ku
Technical Team Members: Nicholas Ursini