TL;DR
A dynamic evolutionary attack detection scheme for practical QKD, in which Eve’s attack feature could be vectorized by a well-designed embedding model and dynamically self-update to an attack feature vector database, which significantly improves the generalization capability of quantum attack detection and promotes the practical development of QKD.
This paper proposes a quantum k-nearest neighbor (QkNN)-based multiclass attack detection framework for CVQKD systems that establishes a direct connection between attack detection and secret key generation, enabling a more realistic security evaluation for practical CVQKD systems.
Different from previous approaches that exploit security loopholes to conceal the excess noise introduced by intercept-resend attacks, this scheme can directly obtain raw-key information without introducing additional disturbances.
Federated Learning (FL) enables collaborative model learning without the need to share raw data, but its communication links are vulnerable to interception, replay, and man-in-the-middle (MITM) attacks. The existing key exchange methods rely on computational hardness assumptions, which can be broken by post-quantum att...
This work investigates the security of the NPP TF-QKD protocol in the presence of imperfect intensity modulation, focusing on Trojan-horse attacks and induced photorefractive attacks, and establishes a finite-key security proof that explicitly incorporates this source-side information leakage.
The proposed approach, a standard BB84 protocol, will first be formulated and then further enhanced with decoy-state pulse generation to handle the vulnerabilities due to multi-photon pulses, and models like depolarizing noise, amplitude damping, and measurement errors will be used as real-world quantum channels.
Leveraging the a priori information on the instantaneous QBER, the efficiency of information reconciliation in the Decoy-State BB84 protocol is improved, resulting in a secure key that is almost 3% longer for realistic scenarios, with no computational or hardware complexity overhead.
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