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International Journal of Intelligent Information and Database Systems

Quantum convolutional neural networks with new SVM-FE feature selection for robust intrusion detection

Sathish Kumar Selvaperumal, Yogesh B. Gurav, Mukil Alagirisamy

Published Jan 1, 2025• 1 citation
Publisher
DOI
10.1504/ijiids.2025.10071348

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Intrusion detection system (IDS) is a significant mechanism to improve network security. As a promising technique, machine learning (ML) methods has been applied in IDS to obtain high classification accuracy. However, classical ML based IDS methods hit a bottleneck in computing performance in case of huge network traffic and complex high-dimensional data. Due to the parallelism, superposition, entanglement of quantum computing, quantum computing provides a new solution to speed up the classical ML algorithms. This paper proposes a novel IDS scheme based on full-rotation quantum convolutional neural network (FR-QCNN). The key component of the FR-QCNN is the quantum convolution filter, which is composed of coding layer, variational layer and measurement layer. Different from the traditional quantum convolutional neural network, a full-rotation quantum circuit is used in the variational layer of the FR-QCNN, realizing a complete parameter update in the model training. Experiment on dataset from KDD Cup shows that the IDS classification accuracy of FR-QCNN is higher than classical ML models such as convolutional neural network (CNN), decision tree (DT) and support vector machine (SVM), as well as higher than traditional quantum convolutional neural network(QCNN). Meanwhile, FR-QCNN and QCNN have lower space complexity and time complexity than classical ML methods.
Quantum Convolutional Neural Networks (QCNN) migrate the convolution and pooling concepts of classical Convolutional Neural Networks (CNN) to variational quantum circuits, showing potential on Noisy Intermediate-Scale Quantum (NISQ) devices. However, traditional QCNN adopts a single-depth circuit structure, which has limitations in multi-scale feature extraction, and is prone to the barren plateau phenomenon during training, making it difficult to adapt to high-dimensional real-world scenarios such as network intrusion detection. This paper proposes a QCNN architecture based on a multi-path parallel structure. For the network intrusion detection task, it draws on the Inception module to construct three parallel quantum convolution paths: shallow, medium, and deep, achieving hierarchical extraction of multi-scale features; residual connections are introduced to preserve encoded state information and provide gradient shortcuts; a hybrid optimization strategy combining classical Adam and Quantum Natural Gradient (QNG) is adopted to adapt to the heterogeneous parameter space. Experimental validation shows that, on the UNSW-NB15 network intrusion detection dataset, the proposed Multi-Path Parallel Quantum Convolutional Neural Network (MP-QCNN) improves detection accuracy by 3% compared to traditional single-path QCNN and effectively mitigates the barren plateau phenomenon. This design strictly maintains NISQ depth constraints and quantum parallelism advantages, providing an efficient and stable feature extraction framework for high-dimensional tasks such as network intrusion detection.
Abstract Classical machine learning is more susceptible to adversarial examples due to its linear and non-robust nature, which results in a severe degradation of the recognition accuracy of classical machine learning models. Quantum techniques are shown to have a higher robustness advantage and are more resistant to attacks from adversarial examples than classical machine learning. Inspired by the robustness advantage of quantum computing and the feature extraction advantage of convolutional neural networks, this paper proposes a novel variational quantum convolutional neural network model (VQCNN), whose quantum fully connected layer consists of a combination of a quantum filter and a variational quantum neural network to increase the model’s adversarial robustness. The network intrusion detection model based on VQCNN is verified on KDD CUP99 and UNSW-NB datasets. The results show that under the attack of Fast Gradient Sign Method, the decline values of accuracy, precision, and recall rate of the intrusion detection model based on VQCNN are less than those of the other four models, and it has higher adversarial robustness.