Q-AGNN: Quantum-Enhanced Attentive Graph Neural Network for Intrusion Detection

2026-03-23

AI Summary

This work addresses the challenge of effectively modeling complex dependencies among network traffic flows, which has long constrained the accuracy of intrusion detection systems. To this end, we propose a novel approach that integrates parameterized quantum circuits with graph attention mechanisms, introducing quantum-enhanced representations into graph-structured traffic modeling for the first time. By leveraging quantum-induced embeddings in Hilbert space, our method enables second-order polynomial graph filtering. Extensive experiments demonstrate that the proposed model achieves state-of-the-art or competitive detection performance across four benchmark datasets. Furthermore, we validate its practical feasibility on real noisy intermediate-scale quantum (NISQ) hardware, where it maintains a low false positive rate despite hardware noise, thereby significantly improving both the accuracy and robustness of anomaly detection.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Security and Privacy: Large-scale security measurements

Abstract

With the rapid growth of interconnected devices, accurately detecting malicious activities in network traffic has become increasingly challenging. Most existing deep learning-based intrusion detection systems treat network flows as independent instances, thereby failing to exploit the relational dependencies inherent in network communications. To address this limitation, we propose Q-AGNN, a Quantum-Enhanced Attentive Graph Neural Network for intrusion detection, where network flows are modeled as nodes and edges represent similarity relationships. Q-AGNN leverages parameterized quantum circuits (PQCs) to encode multi-hop neighborhood information into a high-dimensional latent space, inducing a bounded quantum feature map that implements a second-order polynomial graph filter in a quantum-induced Hilbert space. An attention mechanism is subsequently applied to adaptively weight the quantum-enhanced embeddings, allowing the model to focus on the most influential nodes contributing to anomalous behavior. Extensive experiments conducted on four benchmark intrusion detection datasets demonstrate that Q-AGNN achieves competitive or superior detection performance compared to state-of-the-art graph-based methods, while consistently maintaining low false positive rates under hardware-calibrated noise conditions. Moreover, we also executed the Q-AGNN framework on actual IBM quantum hardware to demonstrate the practical operability of the proposed pipeline under real NISQ conditions. These results highlight the effectiveness of integrating quantum-enhanced representations with attention mechanisms for graph-based intrusion detection and underscore the potential of hybrid quantum-classical learning frameworks in cybersecurity applications.

Problem

• intrusion detection
• graph neural network
• network traffic
• relational dependencies
• malicious activities

Innovation

• Quantum-Enhanced GNN
• Parameterized Quantum Circuits
• Graph Attention Mechanism
• Intrusion Detection
• NISQ Hardware