# Quantum Autoencoders for Anomaly Detection in Cybersecurity

arXiv: https://arxiv.org/abs/2510.21837 · HTML: https://mlsanity.com/paper/2510.21837/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 24 references that are in the map.
- First posted on arXiv 22 Oct 2025.

## Builds on (most cited first, 5 of 5)

- [Autoencoding beyond pixels using a learned similarity metric](https://mlsanity.com/paper/1512.09300/) (2015)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel](https://mlsanity.com/paper/2308.00583/) (2023)
- [Applying Quantum Autoencoders for Time Series Anomaly Detection](https://mlsanity.com/paper/2410.04154/) (2024)
- [The role of data embedding in quantum autoencoders for improved anomaly detection](https://mlsanity.com/paper/2409.04519/) (2024)

## Built on by (most cited first, 1 of 1)

- [Intelligence-Guided Adaptive Purification for DDoS-Resilient Quantum Networks: A CUDA-Q based Study](https://mlsanity.com/paper/2607.16276/) (2026)

## Abstract (arXiv)

Anomaly detection in cybersecurity is a challenging task, where normal events far outnumber anomalous ones with new anomalies occurring frequently. Classical autoencoders have been used for anomaly detection, but struggles in data-limited settings which quantum counterparts can potentially overcome. In this work, we apply Quantum Autoencoders (QAEs) for anomaly detection in cybersecurity, specifically on the BPF-extended tracking honeypot (BETH) dataset. QAEs are evaluated across multiple encoding techniques, ansatz types, repetitions, and feature selection strategies. Our results demonstrate that an 8-feature QAE using Dense-Angle encoding with a RealAmplitude ansatz can outperform Classical Autoencoders (CAEs), even when trained on substantially fewer samples. The effects of quantum encoding and feature selection for developing quantum models are demonstrated and discussed. In a data-limited setting, the best performing QAE model has a F1 score of 0.87, better than that of CAE (0.77). These findings suggest that QAEs may offer practical advantages for anomaly detection in data-limited scenarios.