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Anomaly detection in Endpoint Detection and Response (EDR) is a critical taskin cybersecurity programs of large companies. With rapidly growing amounts ofdata and the omnipresence of zero-day attacks, manual and rule-based detectiontechniques are no longer eligible in practice. While classical machine learningapproaches to this problem exist, they frequently show unsatisfactoryperformance in differentiating malicious from benign anomalies. A promisingapproach to attain superior generalization than currently employed machinelearning techniques are quantum generative models. Allowing for the largestrepresentation of data on available quantum hardware, we investigate QuantumAnnealing based Quantum Boltzmann Machines (QBMs) for the given problem. Wecontribute the first fully unsupervised approach for the problem of anomalydetection using QBMs and evaluate its performance on an EDR inspired syntheticdataset. Our results indicate that QBMs can outperform their classical analog(i.e., Restricted Boltzmann Machines) in terms of result quality and trainingsteps in special cases. When employing Quantum Annealers from D-Wave Systems,we conclude that either more accurate classical simulators or substantiallymore QPU time is needed to conduct the necessary hyperparameter optimizationallowing to replicate our simulation results on quantum hardware.

Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection