Auto-ML Cyber Security Data Analysis Using Google, Azure and IBM Cloud Platforms

Emmanuel C. Opara, Hayden Wimmer, Carl Rebman

20222022 International Conference on Electrical, Computer and Energy Technologies (ICECET)13 citationsDOI

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.

Topics & Concepts

IBMCloud computingComputer scienceThread (computing)Operating systemComputer securityDatabaseNanotechnologyMaterials scienceNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesAnomaly Detection Techniques and Applications