Auto-ML Cyber Security Data Analysis Using Google, Azure and IBM Cloud Platforms
Document Type
Article
Publication Date
9-9-2022
Publication Title
International Conference on Electrical, Computer and Energy Technologies (ICECET) Proceedings
DOI
10.1109/ICECET55527.2022.9872782
Abstract
Machine Learning can be used with cybersecurity data to protect organizations by using artificial intelligence (AI) to generate rules and models for thread detection. Cloud platforms offer the ability to scale AI efforts as well as automatically generate machine learning models (Auto ML). Adoption of Auto-ML is increasing which is resulting in rapid improvements of the technology. The objective of this paper is to demonstrate the Auto ML functionalities against cyber security threat detection using available tools in free tier accounts created on three different cloud platforms (Microsoft Azure, Google, and IBM). We determined the performance of these tools by the evaluating the optimization speed and accuracy results. A comparison of the advantages of each of the results from the different platforms are presented. Overall, all three platforms performed greater than 70% accuracy with the IBM Cloud Platform having the strongest performance.
Recommended Citation
Opara, Emmanuel C., Hayden Wimmer, Carl M. Rebman Jr.
2022.
"Auto-ML Cyber Security Data Analysis Using Google, Azure and IBM Cloud Platforms."
International Conference on Electrical, Computer and Energy Technologies (ICECET) Proceedings: IEEE Xplore.
doi: 10.1109/ICECET55527.2022.9872782
https://digitalcommons.georgiasouthern.edu/information-tech-facpubs/168
Comments
Georgia Southern University faculty member, Hayden Wimmer co-authored Auto-ML Cyber Security Data Analysis Using Google, Azure and IBM Cloud Platforms.