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International Multi-Topic ICT Conference (IMTIC)

Quantum-Optimized Feature Selection for Enhanced AI-Driven Cybersecurity

Asad Raza, Syeda Sabeeka Fatima

Published Dec 29, 2025• 0 citations
Publisher

Abstract

Machine learning Intrusion detection systems (IDS) fail with large feature spaces and imbalanced attack data. At the same time, current quantum devices are limited by noise and small qubit counts. In this work, we explore a simple hybrid approach in which quantum circuits are used only for feature transformation, while a classical model performs the final classification. Normalized NSL-KDD traffic records are first reduced by principal component analysis (PCA), then encoded into quantum states and processed by a low-depth variational quantum circuit (VQC). The measured outputs form a compact feature vector that is fed to a support vector machine (SVM). On the NSL-KDD dataset, the proposed VQC+SVM model reaches $80.9 \%$ accuracy and an F1-score of 0.814, which is higher than PCA+SVM ($\mathbf{7 3 . 0 \%}$) and autoencoder+SVM ($\mathbf{7 5 . 7 \%}$). An ablation study over different circuit depths and qubit counts shows that a 4-qubit, 3-layer circuit provides the most stable performance under NISQ-like conditions.

DOI
10.1109/imtic68267.2025.11520539

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Machine learning Intrusion detection systems (IDS) fail with large feature spaces and imbalanced attack data. At the same time, current quantum devices are limited by noise and small qubit counts. In this work, we explore a simple hybrid approach in which quantum circuits are used only for feature transformation, while a classical model performs the final classification. Normalized NSL-KDD traffic records are first reduced by principal component analysis (PCA), then encoded into quantum states and processed by a low-depth variational quantum circuit (VQC). The measured outputs form a compact feature vector that is fed to a support vector machine (SVM). On the NSL-KDD dataset, the proposed VQC+SVM model reaches 80.9%80.9 \%80.9% accuracy and an F1-score of 0.814, which is higher than PCA+SVM (73.0%\mathbf{7 3 . 0 \%}73.0%) and autoencoder+SVM (75.7%\mathbf{7 5 . 7 \%}75.7%). An ablation study over different circuit depths and qubit counts shows that a 4-qubit, 3-layer circuit provides the most stable performance under NISQ-like conditions.
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.
Quantum Neural Network (QNN) provides an exciting alternative to machine learning as they take advantage of quantum computer properties when making decisions and processing data. However, the integration of high-dimensional classical data with the limited number of qubits available on current Noisy Intermediate-Scale Quantum computers presents a major difficulty. This paper presents a complete hybrid quantum-classical architecture in Qiskit that utilizes the Network Security Laboratory-Knowledge Discovery and Data Mining (NSL-KDD) dataset to classify network intrusions among multiple classes. In order to work around the exponential cost of computations and reduce the number of times quantum circuits need to be run due to hardware limitations, a subset of the dataset will be created to use during both training and testing. The architecture proposed in this paper uses a four-qubit Variational Quantum Circuit (VQC). First, a classical feature vector is used to create a quantum Hilbert space via a ZZFeatureMap, which initializes the qubits via a Hadamard transformation followed by phase rotation gates that create controlled-entanglement between pairs of features. The quantum states are then passed through a RealAmplitudes variational circuit, while the classical layer optimizes the parameters using the Constrained Optimization BY Linear Approximations (COBYLA) algorithm. The overall accuracy of the model when tested on five classes of intrusion detection results in an accuracy level of 59%, with very strong classification accuracies for both Denial of Service (DoS) and User-to-Root (U2R) attacks as demonstrated by their F1 scores. The proposed QNN achieves a highly competitive baseline of 86% accuracy and 86% recall in specific configurations. These results underscore the potential of shallow, lower-complexity QNN architectures as an emergent tool for cybersecurity in the quantum era.
ABSTRACT The amount of cyber threats is growing at an exponential rate, and intelligent network intrusion detection systems (NIDS) that can cope with high dimensional and complex network data, is essential. Traditional machine‐learning (ML) approaches might not be as effective to identify complex hidden correlations between traffic attributes and at identifying advanced and unseen attacks. In this paper we suggest a classical–quantum hybrid kernel framework that enhances network intrusion detection. Using principal component analysis (PCA), network traffic features are normalized and dimensionality reduced to be compatible with noisy intermediate‐scale quantum (NISQ) devices, whilst preserving over 95% of the variance in the data. The classification is achieved by a fidelity‐based quantum kernel with a classical support vector machine(SVM). The proposed hybrid quantum kernel support vector machine (QK‐SVM)is tested using two datasets, CIC‐IDS2017 and CICIoT2023. Experimental results, achieving accuracy up to 98.1%, F1‐score up to 0.98, and anomaly detection rate of 97.6% with linear and RBF‐SVM baselines, are comparable to that of Random Forest. The results are based on the Qiskit Aer simulator and do not reflect the performance on actual quantum devices.
Weaknesses arising from heterogeneous device structure, high-dimensional traffic patterns, and more complex nonlinear attack behaviors. The traditional intrusion detection systems (IDS), which rely on the classical machine learning models, often face the challenges of scalability, feature explosion, and low representational ability when handling large-scale IoT traffic data. To meet these challenges, we propose a Hybrid Quantum-Classical Variational Neural Network (HQCVNN) for effective intrusion detection in IoT systems. The model utilizes a Variational Quantum Circuit (VQC) as a nonlinear quantum feature map followed by dense neural layers for overall classification. Angle embedding with entanglement layers enables the encoding of quantum data and leads to a better separability of features in high-dimensional space, whilst the classical optimization guarantees efficient training on NISQ time scales. The model is implemented using Qiskit and evaluated on classical benchmark intrusion detection datasets. Experimental results show the proposed method has better classification accuracy, F1-score, and complexity reduction than conventional ANNs and SVMs. The results demonstrate the promise of hybrid quantum machine learning systems with end-to-end general-purpose capabilities in future IoT security and provide pragmatic impressions on how to deploy them on near-term quantum processors.
Intrusion Detection Systems (IDS) rely on intelligent learning techniques to identify complex and evolving attack patterns within network traffic. This paper presents a comparative study of Support Vector Machines (SVM), Quantum Support Vector Machines (QSVM), and Variational Quantum Classifiers (VQC) in the context of intrusion detection. A unified evaluation framework is employed to compare these models based on feature representation, computational complexity, scalability, hardware requirements, and deployment feasibility. The analysis examines how QSVM and VQC utilize quantum feature mapping and parameterized quantum circuits to improve data representation capabilities. Nevertheless, their practical adoption remains limited by the constraints of current Noisy Intermediate-Scale Quantum (NISQ) devices, including hardware noise, restricted qubit resources, and scalability limitations. Conversely, SVM continues to be a dependable and scalable solution for present-day intrusion detection systems. The study further identifies the absence of a standardized framework for evaluating classical and quantum approaches as an important research gap. Overall, the findings suggest that hybrid quantum–classical learning frameworks provide the most realistic path toward adopting quantum machine learning for future cybersecurity solutions
Modern cyberattacks are increasingly complex, posing significant challenges to classical machine learning methods, particularly when labeled data is limited and feature interactions are highly non-linear. In this study we investigates the potential of hybrid quantum-classical learning to enhance feature representations for intrusion detection and explore possible quantum advantages in cybersecurity analytics. Using the UNSW-NB15 dataset, network traffic is transformed into structured feature vectors through classical preprocessing and normalization. Classical models, including Logistic Regression and Support Vector Machines with linear and RBF kernels, are evaluated on the full dataset to establish baseline performance under large-sample conditions. Simultaneously, a quantum-enhanced pipeline maps classical features into variational quantum circuits via angle encoding and entangling layers, executed on a CPU-based quantum simulator, with resulting quantum embeddings classified using a classical SVM. Experiments show that while classical models achieve higher overall accuracy with large datasets, quantum-enhanced representations demonstrate superior attack recall and improved class separability when data is scarce, suggesting that quantum feature spaces capture complex correlations inaccessible to shallow classical models. These results highlight the potential of quantum embeddings to improve generalization and representation quality in cybersecurity tasks and provide a reproducible framework for evaluating quantum advantages as quantum hardware and simulators continue to advance.