The Intersection of Machine Learning and Cybersecurity

Kulda, Kevin

The Intersection of Machine Learning and Cybersecurity

dc.contributor.advisor	Pirko, Matthew
dc.contributor.author	Kulda, Kevin
dc.contributor.department	Baylor Business Fellows.	en_US
dc.contributor.other	Baylor University.	en_US
dc.contributor.schools	honors college	en_US
dc.date.accessioned	2020-05-20T12:45:00Z
dc.date.available	2020-05-20T12:45:00Z
dc.date.copyright	2020
dc.date.issued	2020-05-20
dc.description.abstract	This paper will examine the intersection of cybersecurity and machine learning. Use cases integrating machine learning for both defensive and offensive cybersecurity will be surveyed. Within defensive cybersecurity, this paper will investigate how machine learning is being used to protect against external threats and internal threats. To show an interesting way machine learning may be used in a cyber attack, this paper will look at a Prime+Probe cache side-channel attack that aims to learn which machine learning transfer model a program is running. From an external perspective, the analysis will show how the side-channel attack may be implemented, and how it can be defended against. Finally, we propose an additional method to detect and prevent this attack on an internal network.	en_US
dc.identifier.uri	https://hdl.handle.net/2104/10865
dc.language.iso	en_US	en_US
dc.rights	Baylor University projects are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Contact libraryquestions@baylor.edu for inquiries about permission.	en_US
dc.rights.accessrights	Worldwide access	en_US
dc.subject	Machine Learning.	en_US
dc.subject	Cybersecurity.	en_US
dc.subject	Transfer Learning.	en_US
dc.title	The Intersection of Machine Learning and Cybersecurity	en_US
dc.type	Thesis	en_US