Empirical Risk-aware Machine Learning on Trojan-Horse Detection for Trusted Quantum Key Distribution Networks

TL;DR

This paper introduces a risk-aware machine learning approach to detect Trojan-horse attacks in quantum key distribution networks, enhancing practical security by evaluating and mitigating risks over optical quantum channels.

Contribution

It proposes a novel risk analysis framework for Trojan-horse attack detection in QKD, integrating machine learning with real-world optical quantum channels for improved security.

Findings

The proposed method effectively detects Eve's attacks over 30km optical channels.

The classifier can identify latent Eve attacks with high probability.

The Eve detection probability is mathematically bounded in the scenario.

Abstract

Quantum key distribution (QKD) is a cryptographic technique that leverages principles of quantum mechanics to offer extremely high levels of data security during transmission. It is well acknowledged for its capacity to accomplish provable security. However, the existence of a gap between theoretical concepts and practical implementation has raised concerns about the trustworthiness of QKD networks. In order to mitigate this disparity, we propose the implementation of risk-aware machine learning techniques that present risk analysis for Trojan-horse attacks over the time-variant quantum channel. The trust condition presented in this study aims to evaluate the offline assessment of safety assurance by comparing the risk levels between the recommended safety borderline. This assessment is based on the risk analysis conducted. Furthermore, the proposed trustworthy QKD scenario demonstrates its numerical findings with the assistance of a state-of-the-art point-to-point QKD device, which operates over optical quantum channels spanning distances of 1m, 1km, and 30km. Based on the results from the experimental evaluation of a 30km optical connection, it can be concluded that the QKD device provided prior information to the proposed learner during the non-existence of Eve's attack. According to the optimal classifier, the defensive gate offered by our learner possesses the capability to identify any latent Eve attacks, hence effectively mitigating the risk of potential vulnerabilities. The Eve detection probability is provably bound for our trustworthy QKD scenario.