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Quantum Machine Learning for Security Data Analysis
Conference proceeding   Peer reviewed

Quantum Machine Learning for Security Data Analysis

Dhanunjai Bandi, Yong Shi, Hossain Shahriar, Dan Lo, Kun Suo, Hongmei Chi and Kai Qian
2023 IEEE World AI IoT Congress (AIIoT), pp.0460-0465
World AI IoT Congress (AIIoT) (Seattle, Washington, USA, 06/07/2023–06/10/2023)
07/2023
DOI: https://doi.org/10.1109/AIIoT58121.2023.10174430

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Abstract

Logistic regression Neural Networks Quantum Machine Learning Support Vector Machine classical machine learning Cybersecurity Data Analysis Machine Learning
In this paper, we apply Quantum Machine Learning to analyze security datasets. We compare cross-models, Quantum Machine Learning (QML) against Classical Machine Learning (CML), performance with increasing data size, and performance with increasing iteration numbers using commonly used machine learning techniques such as Neural Networks (NN), Support Vector Machines (SVM), and Logistic Regression (LR). Our study focuses on assessing the accuracy of QML and CML approaches on real-world security datasets. The results provide light on the advantages and disadvantages of both QML and CML methodologies, with implications for their use in security data analysis. The experimental findings provide useful information on the applicability of QML and CML for security-related applications. The study contributes to the growing field of quantum machine learning research, particularly in the context of security data analysis, and offers helpful advice for academics and practitioners working in this area.

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