Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection

Jonas Stein; Daniëlle Schuman; Magdalena Benkard; Thomas Holger; Wanja Sajko; Michael Kölle; Jonas Nüßlein; Leo Sünkel; Olivier Salomon; Claudia Linnhoff-Popien

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Paper

Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection

Topics: Quantum Artificial Intelligence

In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, 177-185, 2024 , Rome, Italy

Authors: Jonas Stein ¹ ; Daniëlle Schuman ¹ ; Magdalena Benkard ¹ ; Thomas Holger ¹ ; Wanja Sajko ¹ ; Michael Kölle ¹ ; Jonas Nüßlein ¹ ; Leo Sünkel ¹ ; Olivier Salomon ² and Claudia Linnhoff-Popien ¹

Affiliations: ¹ LMU Munich, Germany ; ² Allianz, France

Keyword(s): Quantum Boltzmann Machine, Quantum Annealing, Anomaly Detection.

Abstract: Anomaly detection in Endpoint Detection and Response (EDR) is a critical task in cybersecurity programs of large companies. With rapidly growing amounts of data and the omnipresence of zero-day attacks, manual and rule-based detection techniques are no longer eligible in practice. While classical machine learning approaches to this problem exist, they frequently show unsatisfactory performance in differentiating malicious from benign anomalies. A promising approach to attain superior generalization compard to currently employed machine learning techniques is using quantum generative models. Allowing for the largest representation of data on available quantum hardware, we investigate Quantum-Annealing-based Quantum Boltzmann Machines (QBMs) for the given problem. We contribute the first fully unsupervised approach for the problem of anomaly detection using QBMs and evaluate its performance on an EDR-inspired synthetic dataset. Our results indicate that QBMs can outperform their classi cal analog (i.e., Restricted Boltzmann Machines) in terms of result quality and training steps in special cases. When employing Quantum Annealers from D-Wave Systems, we conclude that either more accurate classical simulators or substantially more QPU time is needed to conduct the necessary hyperparameter optimization allowing to replicate our simulation results on quantum hardware. (More)

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Paper citation in several formats:

Stein, J.; Schuman, D.; Benkard, M.; Holger, T.; Sajko, W.; Kölle, M.; Nüßlein, J.; Sünkel, L.; Salomon, O. and Linnhoff-Popien, C. (2024). Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4; ISSN 2184-433X, SciTePress, pages 177-185. DOI: 10.5220/0012326100003636

@conference{icaart24,
author={Jonas Stein. and Daniëlle Schuman. and Magdalena Benkard. and Thomas Holger. and Wanja Sajko. and Michael Kölle. and Jonas Nüßlein. and Leo Sünkel. and Olivier Salomon. and Claudia Linnhoff{-}Popien.},
title={Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={177-185},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012326100003636},
isbn={978-989-758-680-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection
SN - 978-989-758-680-4
IS - 2184-433X
AU - Stein, J.
AU - Schuman, D.
AU - Benkard, M.
AU - Holger, T.
AU - Sajko, W.
AU - Kölle, M.
AU - Nüßlein, J.
AU - Sünkel, L.
AU - Salomon, O.
AU - Linnhoff-Popien, C.
PY - 2024
SP - 177
EP - 185
DO - 10.5220/0012326100003636
PB - SciTePress