## 4 Results and Discussions
### Performance Analysis
In Figure 3b, the suggested 2-EHRIO achieved a superior efficiency in error correction (0.95) and privacy amplification (0.95). Concurrently, the current methodologies, specifically EHO, SSO, ALO, and CSO, achieved diminished efficiencies, with error corrections of 0.93, 0.90, 0.86, and 0.80, respectively. The current algorithms showed inferior efficiencies in  
privacy amplification relative to the suggested technique. The improved performance of 2-EHRIO was due to the implementation of Elitist Random Immigrants within the EHO algorithm. Consequently, the proposed model effectively optimized the system parameters within the CV-QKD system. * Performance analysis for side-channel attack detection: Here, the analysis of ADS-CNN was performed using an existing CNN, Gated Recurrent Unit (GRU), Long Short Term Memory (LSTM), and Deep Neural Network (DNN). Table 2 illustrates the performance of the proposed ADS-CNN regarding accuracy and sensitivity. The proposed ADS-CNN attained an accuracy of 99.05% and a sensitivity of 99.07%. However, in comparison to the proposed methodology, the accuracy and sensitivity of the existing methods, including CNN, GRU, LSTM, and DNN, were inferior. Consequently, the proposed methodology accurately anticipated the side-channel attacks. The suggested ADS-CNN was compared to the baseline CNN, GRU, LSTM, and DNN models in terms of specificity, recall, F1-score, and precision (Figure 4). As demonstrated, ADS-CNN attained much higher levels of accuracy (99.04% precision, 99.997% recall, 99.55% F1-score, and 90.04% specificity) compared to the baselines (93.88% precision, 93.73% recall, 93.8% F1-score, and 93.87% specificity). This improvement in performance was due to the incorporation of adaptive depthwise separable convolutions, which enhanced discriminative feature extraction and decreased parameter redundancy, leading to higher accuracy and resilient classification of side-channel attacks. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Methods** & **Accuracy (\%)** & **Sensitivity (\%)** \\ \hline Proposed ADS-CNN & 99.05 & 99.07 \\ CNN & 97.82 & 97.74 \\ GRU & 94.15 & 94.05 \\ LSTM & 92.47 & 92.41 \\ DNN & 90.75 & 90.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy and sensitivity analysis. Figure 3: Performance analysis of the proposed 2-EHRIO model in terms of (**a**) key rate and secret key capacity, (**b**) error correction and privacy amplification. Performance analysis for authentication: Here, analysis of the proposed CHMAC-EPQH's performance was performed using the prevailing HMAC, Secure Hashing Algorithm-256 (SHA-256), Message Digest-5 (MD5), and Blake2. Figure 5 illustrates the performance of the proposed CHMAC-EPQH in authentication, specifically for the False Acceptance Rate (FAR) and False Rejection Rate (FRR). Regarding FAR and FRR, the proposed approach achieved 5.794% and 0.1751%; while, the existing methods, such as HMAC, SHA-256, MD5, and Blake2 exhibited higher values, indicating poor performance when compared to the proposed method. The suggested method's enhanced performance resulted from the incorporation of compression and encoding processes within the HMAC algorithm. Consequently, the CHMAC-EPQH showed proficiently authentication of both the device and the communication channel. * Performance analysis for link failure prediction: Here, the proposed RDBSCAN's performance was compared against the prevailing DBSCAN, K-Means Clustering (KMC), Affinity Propagation (AP), and Gaussian Mixture Model (GMM). The performance analysis of the suggested RDBSCAN, focusing on clustering time and Jaccard Index, is illustrated in Table 3. The proposed technique required a minimum clustering duration of 0.005994 s. The existing approaches, specifically DBSCAN, KMC, AP, and GMM, recorded maximum clustering times of 0.0060 s, 0.01100 s, 0.02248 s, and 0.50199 s, respectively. Similarly, the proposed RDBSCAN attained a superior Jaccard Index of 0.543541. However, in comparison to the proposed strategy, the previous algorithms achieved a diminished Jaccard Index.