Quantum Machine Learning for Cyberattack Prediction

Abstract

The proposed study presents a comparative cyberattack prediction framework integrating six classical machine learning models and a Quantum Machine Learning (QML) model. Experimental evaluation on the NSL-KDD dataset demonstrates strong predictive performance, with ensemble and neural approaches showing high testing accuracy, while the QML framework provides enhanced high-dimensional feature representation and promising anomaly detection capability for future zero-day threat prediction. The novelty of this work lies in the unified benchmarking of classical, deep learning, and quantum learning paradigms within a single cybersecurity pipeline. The rapid growth of interconnected digital infrastructures, including enterprise networks, cloud systems, and Internet of Things (IoT) environments, has significantly increased the scale and sophistication of cyber threats. Traditional machine learning models have shown strong capabilities in intrusion detection and anomaly analysis; however, they increasingly face limitations when dealing with high dimensional network traffic, zero-day exploits, and complex nonlinear attack patterns. Quantum Machine Learning (QML), which combines principles of quantum computing with statistical learning, has emerged as a promising paradigm for next-generation cyberattack prediction. This paper presents a comprehensive hybrid quantum-classical framework for cyberattack prediction using the NSL-KDD dataset. The proposed study integrates classical machine learning models such as Support Vector Machine (SVM), Logistic Regression, Naive Bayes, Multi-Layer Perceptron (MLP), Random Forest, and Convolutional Neural Networks (CNN), alongside a Quantum Machine Learning pipeline implemented using PennyLane. The framework incorporates preprocessing, dimensionality reduction using Principal Component Analysis (PCA), quantum angle encoding, variational quantum circuits, and comparative performance analysis. Experimental findings indicate that QML demonstrates strong potential for identifying complex attack signatures and improving prediction robustness under high-dimensional conditions. The paper further discusses implementation challenges, scalability, quantum noise constraints, and future research directions.

Introduction

This research presents a hybrid quantum-classical framework for cyberattack prediction using the NSL-KDD benchmark dataset. The motivation stems from the rapid growth of cyber threats such as DDoS attacks, phishing, malware, ransomware, and zero-day exploits, which challenge traditional machine learning (ML) models due to the increasing volume and complexity of network traffic. To address these limitations, the study explores Quantum Machine Learning (QML), which leverages quantum computing concepts such as superposition and entanglement to improve the analysis of high-dimensional cybersecurity data.

The proposed framework compares six classical ML and deep learning models—Support Vector Machine (SVM), Logistic Regression, Naive Bayes, Random Forest, Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN)—with a Quantum Machine Learning model implemented using PennyLane and variational quantum circuits (VQCs). The dataset undergoes preprocessing through label encoding and feature scaling, while Principal Component Analysis (PCA) reduces feature dimensions before quantum encoding onto four qubits.

Experimental results show that Random Forest and MLP achieve the highest accuracy among classical models, while CNN effectively captures nonlinear traffic patterns. The QML model demonstrates promising potential by mapping data into a richer quantum feature space, enabling improved detection of complex and previously unseen cyberattacks. The study also reports that QML achieves 2–3× faster training times than several classical models in simulation, highlighting its potential for real-time intrusion detection.

Despite these promising findings, the research acknowledges limitations such as simulator-based implementation, limited qubit availability, quantum noise, and reliance on the NSL-KDD dataset. Future work includes testing on more recent cybersecurity datasets (e.g., CICIDS2017, CICIDS2018, and UNSW-NB15), deploying models on real quantum hardware, exploring deeper quantum circuits and quantum kernel methods, and developing hybrid quantum-classical ensemble models for enhanced cybersecurity applications.

Conclusion

A. Summary of Findings This study demonstrates that Quantum Machine Learning provides a promising direction for next-generation cyberattack prediction through the implementation of a hybrid quantum-classical framework. The research successfully benchmarks multiple classical machine learning, deep learning, and quantum learning models on a cybersecurity dataset. B. Practical Implications The experimental comparison confirms that ensemble models such as Random Forest and nonlinear architectures such as MLP and CNN provide strong predictive baselines. At the same time, the QML model introduces a highly innovative computational paradigm capable of representing complex feature interactions within a quantum state space. C. Final Conclusion Although current quantum implementations remain simulator-based and constrained by hardware limitations, the findings strongly indicate that QML can play a major role in future intelligent cybersecurity infrastructures. This research establishes a strong academic and practical foundation for future work in quantum enhanced cyberattack prediction.

References

[1] J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, \"Quantum machine learning,\" Nature, vol. 549, no. 7671, pp. 195–202, 2017. [2] P. Lamichhane and D. B. Rawat, \"Quantum machine learning: recent advances, challenges, and perspectives,\" IEEE Access, vol. 13, pp. 94086–94105, 2025. [3] M. Kalinin and V. Krundyshev, \"Security intrusion detection using quantum machine learning techniques,\" Journal of Computer Virology and Hacking Techniques, vol. 19, no. 1, pp. 125–136, 2023. [4] O. K. Nicesio and A. G. Leal, \"Quantum machine learning for network intrusion detection systems: a systematic literature review,\" in Proc. IEEE ICAIC, 2023. [5] I. H. Witten, E. Frank, and M. A. Hall, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2016. [6] T. Tavallaee et al., \"A detailed analysis of the KDD CUP 99 dataset,\" in Proc. IEEE CISDA, 2009. [7] S. Axelsson, \"Intrusion detection systems: a survey and taxonomy,\" Technical Report, Chalmers University, 2000. [8] S. Schuld and F. Petruccione, Supervised Learning with Quantum Computers. Springer, 2018. [9] V. Dunjko and H. J. Briegel, \"Machine learning & artificial intelligence in the quantum domain,\" Reports on Progress in Physics, vol. 81, no. 7, 2018. [10] M. Schuld, A. Bocharov, K. Svore, and N. Wiebe, \"Circuit centric quantum classifiers,\" Physical Review A, vol. 101, 2020. [11] M. Benedetti, E. Lloyd, S. Sack, and M. Fiorentini, \"Parameterized quantum circuits as machine learning models,\" Quantum Science and Technology, vol. 4, 2019. [12] M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, \"NSL-KDD dataset for network intrusion detection,\" 2009. [13] V. Havlí?ek et al., \"Supervised learning with quantum-enhanced feature spaces,\" Nature, vol. 567, pp. 209–212, 2019. [14] M. Schuld, R. Sweke, and J. Meyer, \"The effect of data encoding on the expressive power of variational quantum machine learning models,\" Physical Review A, vol. 103, 2021. [15] F. Arute et al., \"Quantum supremacy using a programmable superconducting processor,\" Nature, vol. 574, pp. 505–510, 2019. [16] E. Farhi and H. Neven, \"Classification with quantum neural networks on near term processors,\" arXiv:1802.06002, 2018. Supervised [17] S. Lloyd, M. Mohseni, and P. Rebentrost, \"Quantum algorithms for and unsupervised machine learning,\" arXiv:1307.0411, 2013. [18] A. Abbas et al., \"The power of quantum neural networks,\" Nature Computational Science, vol. 1, pp. 403–409, 2021. [19] M. Cerezo et al., \"Variational quantum algorithms,\" Nature Reviews Physics, vol. 3, pp. 625–644, 2021. [20] D. Silver et al., \"Mastering the game of Go with deep neural networks and tree search,\" Nature, vol. 529, pp. 484–489, 2016.

Copyright

Copyright © 2026 Nikhil Kinekar, Minal Dandekar, Nikhil Agashe, Mrunali Moundekar, Aakansha Bawankar, Rahul Kawariya. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.