Date of Defense
4-23-2025
Date of Graduation
4-2025
Department
Business Information Systems
First Advisor
Ting-Yu (Kevin) Mu
Second Advisor
Alan Rea
Keywords
Generative AI, GenAI, SDN, artificial intelligence, machine learning, cybersecurity, network security, anomaly, GAN, generative adversarial network
Abstract
This paper aims to explore various approaches to using generative artificial intelligence (GenAI) to improve network security in software-defined networking. While software-defined networks provide a more programmable infrastructure, they are not immune to network security threats. Through a combination of Software-Defined Networking (SDN) technologies and generative AI, it is possible to facilitate improved SDN security approaches that promise enhanced network efficiency and protection. Among these approaches, generative adversarial networks (GAN) based models can be employed to generate adversarial traffic samples to train the proposed AI engines proven to be effective in detecting malicious network traffic. Additionally, generative artificial intelligence can be used to perform AI attack simulations and enhance routing optimization, both of which have implications in improving network security.
Recommended Citation
Smith, Anthony, "Using Generative Artificial Intelligence to Improve Software-Defined Network Security: A Brief Survey" (2025). Honors Theses. 3952.
https://scholarworks.wmich.edu/honors_theses/3952
Access Setting
Honors Thesis-Open Access
Defense Presentation