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

Devashish Chaudhary, Sutharshan Rajasegarar, Shiva Raj Pokhrel

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.

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

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.
Paper Structure (22 sections, 28 equations, 5 figures, 6 tables, 1 algorithm)

This paper contains 22 sections, 28 equations, 5 figures, 6 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries and Related Work
  3. Proposed Methodology
  4. Design Considerations
  5. Theoretical Foundations: Quantum Feature Maps and Expressivity
  6. PQC as an Implicit Quantum Feature Map
  7. Expressivity Compared to Classical MLPs
  8. Boundedness and Optimization Stability
  9. Interaction with Polynomial Graph Filtering
  10. Implications for Intrusion Detection
  11. Experimental Setup
  12. Data Preprocessing
  13. Graph Construction
  14. Training Configuration
  15. Results and Discussion
  16. ...and 7 more sections

Figures (5)

  • Figure 1: Overview of the proposed Q-AGNN architecture. Node features are encoded into quantum states via angle encoding and transformed using parameterized quantum circuits. Quantum-enhanced embeddings are aggregated using one-hop and two-hop attention mechanisms, followed by a classical MLP for intrusion detection.
  • Figure 2: Illustration of the network flow graph construction. Each node represents a unique flow, and edges connect nodes with highly similar feature vectors.
  • Figure 3: t-SNE visualization comparing embeddings produced by MLP and PQC. (a)-(b): BoT-IoT dataset (MLP vs PQC). (c)-(d): NF-BoT-IoT dataset (MLP vs PQC). (e)-(f): UNSW-NB15 dataset (MLP vs PQC). (g)-(h): NF-UNSW-NB15 dataset (MLP vs PQC). Green points represent normal traffic and red points represent attack traffic.
  • Figure 4: Train and validation loss curves for four datasets: (a) BoT-IoT, (b) NF-BoT-IoT, (c) UNSW-NB15, (d) NF-UNSW-NB15.
  • Figure 5: Training and testing loss per epoch for Q-AGNN executed on IBM quantum hardware and on a noisy simulator using the same hardware-calibrated noise model. (a) Training loss. (b) Testing loss.