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International Conference on Networking and Network Applications (NaNA)

Quantum SVM Algorithms for Efficient Defense Against Gradient-Based Adversarial Attacks

Hakeem Quadri, Hua Wang, Sardar M. N. Islam, Bo Hu Li, Xin Li

Published Aug 8, 2025• 1 citation
Publisher

Abstract

The growth of machine learning (ML) has revealed model vulnerabilities to adversarial attacks, where small data perturbations degrade performance. Classical defenses often struggle, especially with complex, non-linear data, and adversarial training is resource-intensive due to high computational demands. This research explores the use of quantum kernels in SVMs with ZZ feature maps to transform adversarial data into high-dimensional quantum spaces, showing quantum machine learning’s potential to surpass classical approaches. Testing on MNIST and CIFAR-10 datasets showed quantum SVM achieving 70.6% accuracy on adversarial data, outperforming classical SVM with RBF which achieved $51 \%$. This demonstrates the quantum SVM’s robustness against adversarial attacks.

DOI
10.1109/nana66698.2025.00079

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The growth of machine learning (ML) has revealed model vulnerabilities to adversarial attacks, where small data perturbations degrade performance. Classical defenses often struggle, especially with complex, non-linear data, and adversarial training is resource-intensive due to high computational demands. This research explores the use of quantum kernels in SVMs with ZZ feature maps to transform adversarial data into high-dimensional quantum spaces, showing quantum machine learning’s potential to surpass classical approaches. Testing on MNIST and CIFAR-10 datasets showed quantum SVM achieving 70.6% accuracy on adversarial data, outperforming classical SVM with RBF which achieved 51%51 \%51%. This demonstrates the quantum SVM’s robustness against adversarial attacks.
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Abstract Support vector machines (SVMs) are widely used for classification, but become computationally expensive as datasets grow and data become high-dimensional or nonlinearly separable. Quantum support vector machines (QSVMs) enhance the traditional SVMs by replacing conventional feature mappings with quantum-implemented embeddings that project data into high-dimensional Hilbert spaces, potentially improving expressive power and efficiency. However, training QSVMs on current noisy intermediate-scale quantum (NISQ) devices remains challenging, as constructing the quantum kernel matrix requires repeated circuit executions and scales quadratically with dataset size. We propose NQ-SVM , a hybrid quantum-classical method that incorporates the Nystr\"om approximation to enhance the scalability of QSVMs. By selecting a small set of landmark points, the Nystr\"om method yields a low-rank kernel approximation, reducing complexity from (O(n2))(O(n^2))(O(n2)) to (O(nm))(O(nm))(O(nm)), while preserving the quantum advantages in handling high-dimensional, non-linear data. Extensive simulations demonstrate that NQ-SVM achieves comparable classification accuracy to classical SVMs and full QSVMs, while substantially reducing training time and kernel evaluation costs. These results underscore the potential of NQ-SVM as a scalable and practical hybrid framework for near-term quantum machine learning (QML).
With cyber-attacks becoming increasingly sophisticated, modern network intrusion detection systems (NIDSs) are relying on machine learning (ML) methods for their flexibility in detecting subtle anomalous patterns in huge amounts of network data. However, classical ML methods such as support vector machines (SVMs) often rely on the conversion of low-dimensional data into a high-dimensional space, creating complex linear systems that are time-consuming to evaluate on large data inputs such as network flow logs. We propose addressing this limitation by employing a hybrid quantum-classical ML model to leverage quantum computing's (QC's) superiority in high-dimensional areas. We constructed a quantum kernel with an SVM model and evaluated it on four different network attacks from a modern intrusion detection dataset. Results reveal an average hardware accuracy rate of 85% with noticeably small deviations between runs, suggesting that quantum kernels may be a noise-resistant solution. We evaluated these results alongside classical and noiseless quantum simulator benchmarks.
Quantum machine learning has emerged as a promising paradigm that leverages quantum computational principles to potentially outperform classical approaches on specific learning tasks. This paper presents a comprehensive empirical investigation comparing Quantum Machine Learning (QML), specifically Quantum Support Vector Machines (QSVM) against Classical Machine Learning (CML) baselines including Logistic Regression, K Nearest Neighbors and classical SVMs. We implement quantum kernel methods using variational quantum circuits with angle embedding, IQP and ZZ feature maps on datasets exhibiting varying structural complexity. Our experiments on synthetic quantum generated datasets (Quantum Parity, Quantum Entanglement, Quantum Interference) demonstrate that QSVM achieves superior classification accuracy with improvements ranging from 2.9 % to 60 % over the best classical baselines achieving a 55.6 % overall win rate. Notably, on the Quantum Parity dataset, QSVM consistently outperformed all classical models across multiple configurations. On real-world benchmark datasets (Breast Cancer, Iris, Wine), QSVM Angle achieved 88% accuracy on Breast Cancer compared to 76% for classical SVM Linear a 15.8 % relative improvement while matching the best classical performance on Iris(100%)(100 \%)(100%)and Wine(92%)(92 \%)(92%). All experiments were conducted using quantum circuit simulation on classical hardware. These results provide empirical evidence that quantum kernel methods offer meaningful advantages over classical approaches on datasets with complex feature interactions. We discuss the theoretical foundations of these improvements including quantum feature space expressivity and kernel alignment.
The constant rise of malicious URLs continues to pose significant threats and challenges in cybersecurity, with attackers increasingly evading classical detection methods like blacklists and heuristic-based systems. While machine learning (ML) techniques such as SVMs and CNNs have improved detection, their accuracy and scalability remain limited for emerging adversarial approaches. Quantum machine learning (QML) is a transformative strategy that relies on quantum computation and high-dimensional feature spaces to potentially overcome classical computational limitations. However, the accuracy of QML models such as QSVM and QCNN for URL detection in comparison to classical models remains unexplored. This study evaluates ML models (SVMs and CNNs) and QML models (QSVMs and QCNNs) on a dataset, employing data preprocessing techniques such as outliers, feature scaling and feature selection with ANOVA and PCA. Quantum models utilized ZZFeatureMap and ZFeatureMap for data encoding, to transfer original data to qubits. The achieved results showed that CNNs outperformed QCNNs and QSVMs outperformed SVMs in the performance evaluation, demonstrating a competitive potential of quantum computing. QML shows promise for cybersecurity, particularly given the QSVM’s kernel advantages, but current hardware limits the QCNN’s practicality. The significance of this research is to contribute to the growing body of knowledge in cybersecurity by providing a comparative analysis of classical and quantum ML models for classifying malicious URLs.