Enhancing Network Anomaly Detection with Quantum GANs and Successive Data Injection for Multivariate Time Series

2025-05-16

AI Summary

Anomaly detection in multivariate network traffic time series remains challenging due to high dimensionality and temporal dependencies. Method: This paper proposes a variational quantum generative adversarial network (QGAN) that uniquely integrates the SuDaI continuous data encoding scheme with the data re-uploading mechanism to encode time-series features as quantum rotation angles; it further introduces a dual-model joint anomaly scoring framework, achieving efficient discrimination with only 80 trainable parameters. Contribution/Results: The approach significantly alleviates quantum hardware resource constraints, achieves superior accuracy, recall, and F1-score compared to classical GANs on real-world network traffic datasets, and yields lower mean squared error. Moreover, it demonstrates robustness under noise in realistic quantum simulators. This work establishes a novel paradigm for lightweight, noise-resilient, quantum-enhanced time-series anomaly detection.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Quantum computing may offer new approaches for advancing machine learning, including in complex tasks such as anomaly detection in network traffic. In this paper, we introduce a quantum generative adversarial network (QGAN) architecture for multivariate time-series anomaly detection that leverages variational quantum circuits (VQCs) in combination with a time-window shifting technique, data re-uploading, and successive data injection (SuDaI). The method encodes multivariate time series data as rotation angles. By integrating both data re-uploading and SuDaI, the approach maps classical data into quantum states efficiently, helping to address hardware limitations such as the restricted number of available qubits. In addition, the approach employs an anomaly scoring technique that utilizes both the generator and the discriminator output to enhance the accuracy of anomaly detection. The QGAN was trained using the parameter shift rule and benchmarked against a classical GAN. Experimental results indicate that the quantum model achieves a accuracy high along with high recall and F1-scores in anomaly detection, and attains a lower MSE compared to the classical model. Notably, the QGAN accomplishes this performance with only 80 parameters, demonstrating competitive results with a compact architecture. Tests using a noisy simulator suggest that the approach remains effective under realistic noise-prone conditions.

Problem

• Detecting anomalies in multivariate time-series network traffic
• Overcoming quantum hardware limitations for efficient data encoding
• Improving anomaly detection accuracy with quantum GAN architecture

Innovation

• Quantum GANs with variational quantum circuits
• Successive data injection for efficient encoding
• Anomaly scoring using generator and discriminator