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Stabilized quantum-enhanced SIEM architecture and speed-up through Hoeffding tree algorithms enable quantum cybersecurity analytics in botnet detection

Tehrani, Madjid G.; Sultanow, Eldar; Buchanan, William J.; Amir, Malik; Jeschke, Anja; Houmani, Mahkame; Chow, Raymond; Lemoudden, Mouad

Authors

Madjid G. Tehrani

Eldar Sultanow

Malik Amir

Anja Jeschke

Mahkame Houmani

Raymond Chow



Abstract

For the first time, we enable the execution of hybrid quantum machine learning (HQML) methods on real quantum computers with 100 data samples and real-device-based simulations with 5000 data samples, thereby outperforming the current state of research of Suryotrisongko and Musashi from 2022 who were dealing with 1000 data samples and quantum simulators (pure software-based emulators) only. Additionally, we beat their reported accuracy of 76.8% by an average accuracy of 91.2%, all within a total execution time of 1687 s. We achieve this significant progress through two-step strategy: Firstly, we establish a stable quantum architecture that enables us to execute HQML algorithms on real quantum devices. Secondly, we introduce new hybrid quantum binary classifiers (HQBCs) based on Hoeffding decision tree algorithms. These algorithms speed up the process via batch-wise execution, reducing the number of shots required on real quantum devices compared to conventional loop-based optimizers. Their incremental nature serves the purpose of online large-scale data streaming for domain generation algorithm (DGA) botnet detection, and allows us to apply HQML to the field of cybersecurity analytics. We conduct our experiments using the Qiskit library with the Aer quantum simulator, and on three different real quantum devices from Azure Quantum: IonQ, Rigetti, and Quantinuum. This is the first time these tools are combined in this manner.

Citation

Tehrani, M. G., Sultanow, E., Buchanan, W. J., Amir, M., Jeschke, A., Houmani, M., Chow, R., & Lemoudden, M. (2024). Stabilized quantum-enhanced SIEM architecture and speed-up through Hoeffding tree algorithms enable quantum cybersecurity analytics in botnet detection. Scientific Reports, 14, Article 1732. https://doi.org/10.1038/s41598-024-51941-8

Journal Article Type Article
Acceptance Date Jan 11, 2024
Online Publication Date Jan 19, 2024
Publication Date 2024
Deposit Date Jan 18, 2024
Publicly Available Date Jan 30, 2024
Publisher Nature Publishing Group
Peer Reviewed Peer Reviewed
Volume 14
Article Number 1732
DOI https://doi.org/10.1038/s41598-024-51941-8
Public URL http://researchrepository.napier.ac.uk/Output/3485427

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