  Despite significant advancements in continuous-variable quantum key
distribution (CV-QKD), practical CV-QKD systems can be compromised by various
attacks. Consequently, identifying new attack vectors and countermeasures for
CV-QKD implementations is important for the continued robustness of CV-QKD. In
particular, as CV-QKD relies on a public quantum channel, vulnerability to
communication disruption persists from potential adversaries employing
Denial-of-Service (DoS) attacks. Inspired by DoS attacks, this paper introduces
a novel threat in CV-QKD called the Channel Amplification (CA) attack, wherein
Eve manipulates the communication channel through amplification. We
specifically model this attack in a CV-QKD optical fiber setup. To counter this
threat, we propose a detection and mitigation strategy. Detection involves a
machine learning (ML) model based on a decision tree classifier, classifying
various channel tampering attacks, including CA and DoS attacks. For
mitigation, Bob, post-selects quadrature data by classifying the attack type
and frequency. Our ML model exhibits high accuracy in distinguishing and
categorizing these attacks. The CA attack's impact on the secret key rate (SKR)
is explored concerning Eve's location and the relative intensity noise of the
local oscillator (LO). The proposed mitigation strategy improves the attacked
SKR for CA attacks and, in some cases, for hybrid CA-DoS attacks. Our study
marks a novel application of both ML classification and post-selection in this
context. These findings are important for enhancing the robustness of CV-QKD
systems against emerging threats on the channel.
