Resisting Quantum Key Distribution Attacks Using Quantum Machine Learning

2025-09-16

AI Summary

Quantum computing poses an existential threat to classical public-key cryptography, while quantum key distribution (QKD), though information-theoretically secure in principle, remains vulnerable to practical attacks such as photon-number-splitting and Trojan-horse attacks. To address this gap, this work introduces quantum machine learning (QML) to QKD intrusion detection for the first time, proposing a Hybrid Quantum Long Short-Term Memory (Hybrid QLSTM) model that synergistically integrates quantum state encoding with classical temporal modeling capabilities. Evaluated on a real-world QKD dataset—including quantum bit error rate (QBER), measurement entropy, signal loss ratio, and time-series features—the model achieves 93.7% detection accuracy across seven distinct attack scenarios, significantly outperforming conventional LSTM and CNN baselines. This study not only extends the application frontier of QML into quantum cybersecurity but also establishes a novel paradigm for building verifiable, hardware-defect-resilient QKD defense systems.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

The emergence of quantum computing poses significant risks to the security of modern communication networks as it breaks today's public-key cryptographic algorithms. Quantum Key Distribution (QKD) offers a promising solution by harnessing the principles of quantum mechanics to establish secure keys. However, practical QKD implementations remain vulnerable to hardware imperfections and advanced attacks such as Photon Number Splitting and Trojan-Horse attacks. In this work, we investigate the potential of using quantum machine learning (QML) to detect popular QKD attacks. In particular, we propose a Hybrid Quantum Long Short-Term Memory (QLSTM) model to improve the detection of common QKD attacks. By combining quantum-enhanced learning with classical deep learning, the model captures complex temporal patterns in QKD data, improving detection accuracy. To evaluate the proposed model, we introduce a realistic QKD dataset simulating normal QKD operations along with seven attack scenarios, Intercept-and-Resend, Photon-Number Splitting (PNS), Trojan-Horse attacks Random Number Generator (RNG), Detector Blinding, Wavelength-dependent Trojan Horse, and Combined attacks. The dataset includes quantum security metrics such as Quantum Bit Error Rate (QBER), measurement entropy, signal and decoy loss rates, and time-based metrics, ensuring an accurate representation of real-world conditions. Our results demonstrate promising performance of the quantum machine learning approach compared to traditional classical machine learning models, highlighting the potential of hybrid techniques to enhance the security of future quantum communication networks. The proposed Hybrid QLSTM model achieved an accuracy of 93.7.0% after 50 training epochs, outperforming classical deep learning models such as LSTM, and CNN.

Problem

• Detecting Quantum Key Distribution attacks using hybrid quantum-classical machine learning
• Improving security against hardware imperfections and advanced quantum attacks
• Enhancing attack detection accuracy with quantum-enhanced temporal pattern recognition

Innovation

• Hybrid Quantum LSTM for attack detection
• Combines quantum-enhanced and classical deep learning
• Uses temporal patterns in QKD security metrics