Quantum-Based Multi-Model Machine Learning for Security Data Analysis

In today's rapidly evolving technology era, cybersecurity threats have become sophisticated, challenging conventional detection and defense. Classical machine learning aids early threat detection but lacks real-time data processing and adaptive threat detection due to the reliance on large, clean datasets. New attack techniques emerge daily, and data scale and complexity limit classical computing. Quantum-based machine learning (QML) using quantum computing (QC) offers solutions. QML combines QC and machine learning to analyze big data effectively. This paper investigates multiple QML algorithms and compare their performance with their classical counterparts.