Quantum Machine Learning with Enhanced Autoencoders for Intrusion Detection
Resumo
Intrusion detection remains a critical challenge in cybersecurity, particularly when dealing with high-dimensional data. In this work, we propose a hybrid quantum machine learning model that integrates a Quantum Autoencoder (QAE) for dimensionality reduction with a Quantum Neural Network (QNN) classifier. The QAE compresses input features into latent states without relying on classical methods such as Principal Component Analysis (PCA), preserving quantum-relevant information. These latent states are then classified using a Variational Quantum Circuit implemented with Qiskit. We systematically evaluated different ansatz architectures and hyperparameters on the NSL-KDD dataset. The best configuration, an EstimatorQNN with EfficientSU2 ansatz (4 repetitions), achieved an F1-score of 0.767, with balanced recall (0.70) and precision (0.84). These results highlight the potential of quantum-enhanced models for addressing the scalability challenges of intrusion detection, representing a step toward applying High-Performance Computing principles to cybersecurity.
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C. Bravo-Prieto. Quantum autoencoders with enhanced data encoding. Machine Learning: Science and Technology, 2(3):035028, jul 2021. DOI: 10.1088/2632-2153/ac0616.
K. A. Britt, F. A. Mohiyaddin, and T. S. Humble. Quantum accelerators for high-performance computing systems. In 2017 IEEE International Conference on Rebooting Computing (ICRC), pages 1–7, 2017. DOI: 10.1109/ICRC.2017.8123664.
D. Crawford, A. Hoecker, and D. Aulanier. Technology report 2025, 2025. URL [link]. Accessed: Sept. 27, 2025.
Y. Du, X. Wang, N. Guo, Z. Yu, Y. Qian, K. Zhang, M.-H. Hsieh, P. Rebentrost, and D. Tao. Quantum machine learning: A hands-on tutorial for machine learning practitioners and researchers, 2025. URL [link].
R. Frehner and K. Stockinger. Applying quantum autoencoders for time series anomaly detection, 2024. URL [link].
M. Gabrich, H. Freitas, and M. Souza. Análise de redes neurais para crispr: Uma abordagem com computação quântica. In XXV Simpósio em Sistemas Computacionais de Alto Desempenho, pages 13–24, 2024. DOI: 10.5753/sscad.2024.244778.
M. Hdaib, S. Rajasegarar, and L. Pan. Quantum deep learning-based anomaly detection for enhanced network security. Quantum Machine Intelligence, 6:26, 2024. DOI: 10.1007/s42484-024-00163-2.
C. Huot, S. Heng, T.-K. Kim, and Y. Han. Quantum autoencoder for enhanced fraud detection in imbalanced credit card dataset. IEEE Access, 12:169671–169682, 2024. DOI: 10.1109/ACCESS.2024.3496901.
E. Johnston, N. Harrigan, and M. Gimeno-Segovia. Programming Quantum Computers: Essential Algorithms and Code Samples. O’Reilly Media, 2019.
A. Kukliansky, M. Orescanin, C. Bollmann, and T. Huffmire. Network anomaly detection using quantum neural networks on noisy quantum computers. IEEE Transactions on Quantum Engineering, 5:1–11, 2024. DOI: 10.1109/TQE.2024.3359574.
L. A. Maciel, M. A. Souza, and H. C. Freitas. Reconfigurable fpga-based k-means/k-modes architecture for network intrusion detection. IEEE Transactions on Circuits and Systems II: Express Briefs, 67(8):1459–1463, 2020. DOI: 10.1109/TCSII.2019.2939826.
L. A. Maciel, M. A. Souza, and H. C. Freitas. Energy-efficient cpu+fpga-based cnn architecture for intrusion detection systems. IEEE Consumer Electronics Magazine, 13 (4):65–72, 2024. DOI: 10.1109/MCE.2023.3283730.
S. Mensa, M. E. Sahin, E. Altamura, O. Wallis, S. P. Wood, A. Dekusar, D. A. Millar, T. Imamichi, and A. Matsuo. Qiskit machine learning: An open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators, 2025. URL [link].
G. E. Moore. Cramming more components onto integrated circuits, reprinted from electronics, volume 38, number 8, april 19, 1965, pp.114 ff. IEEE Solid-State Circuits Society Newsletter, 11(3):33–35, 2006. DOI: 10.1109/N-SSC.2006.4785860.
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J. Romero, J. P. Olson, and A. Aspuru-Guzik. Quantum autoencoders for efficient compression of quantum data. Quantum Science and Technology, 2:4, 2017. DOI: 10.1088/2058-9565/aa8072.
R. Vadisetty and A. Polamarasetti. Quantum computing for cryptographic security with artificial intelligence. In 2024 12th International Conference on Control, Mechatronics and Automation, pages 252–260, 2024. DOI: 10.1109/ICCMA63715.2024.10843897.
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M. H. Zaib. Nsl-kdd dataset, 2019. URL [link]. Accessed: Sept. 27, 2025.
K. Zaman, A. Marchisio, M. A. Hanif, and M. Shafique. A survey on quantum machine learning: Current trends, challenges, opportunities, and the road ahead, 2025. URL [link].
A. Zeguendry, Z. Jarir, and M. Quafafou. Quantum machine learning: A review and case studies. Entropy, 25(2):287, 2023. DOI: 10.3390/e25020287.
Y. Zhu, A. Bouridane, M. E. Celebi, D. Konar, P. Angelov, Q. Ni, and R. Jiang. Quantum face recognition with multigate quantum convolutional neural network. IEEE Transactions on Artificial Intelligence, 5(12):6330–6341, 2024. DOI: 10.1109/TAI.2024.3419077.
Publicado
28/10/2025
Como Citar
SOUZA, Milleny Teixeira de; SOUZA, Matheus Alcântara; FREITAS, Henrique Cota de.
Quantum Machine Learning with Enhanced Autoencoders for Intrusion Detection. In: SIMPÓSIO EM SISTEMAS COMPUTACIONAIS DE ALTO DESEMPENHO (SSCAD), 26. , 2025, Bonito/MS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 217-228.
DOI: https://doi.org/10.5753/sscad.2025.16683.
