Quantum Clustering for Cybersecurity
Walid El Maouaki, Nouhaila Innan, Alberto Marchisio, Taoufik Said,, Mohamed Bennai, Muhammad Shafique

TL;DR
This paper introduces a quantum machine learning framework that improves cybersecurity vulnerability clustering, outperforming classical methods in accuracy and providing more precise vulnerability categorization for better risk management.
Contribution
The study develops novel quantum clustering algorithms tailored for cybersecurity data, demonstrating superior performance over classical techniques in vulnerability analysis.
Findings
Quantum algorithms achieved higher clustering quality scores.
Framework effectively categorizes vulnerabilities by severity and vendor.
Results suggest enhanced cybersecurity risk assessment capabilities.
Abstract
In this study, we develop a novel quantum machine learning (QML) framework to analyze cybersecurity vulnerabilities using data from the 2022 CISA Known Exploited Vulnerabilities catalog, which includes detailed information on vulnerability types, severity levels, common vulnerability scoring system (CVSS) scores, and product specifics. Our framework preprocesses this data into a quantum-compatible format, enabling clustering analysis through our advanced quantum techniques, QCSWAPK-means and QkernelK-means. These quantum algorithms demonstrate superior performance compared to state-of-the-art classical clustering techniques like k-means and spectral clustering, achieving Silhouette scores of 0.491, Davies-Bouldin indices below 0.745, and Calinski-Harabasz scores exceeding 884, indicating more distinct and well-separated clusters. Our framework categorizes vulnerabilities into distinct…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Quantum Information and Cryptography · Information and Cyber Security
