# Machine Learning-Assisted Secure Random Communication System
## 6 Results and Discussion
### Intended Communication
#### 6.1.2 Bit Error Rate Analysis  
We checked the trends that developed for the SkaSNSK responsible factors (\(\alpha\), \(\Delta\beta\), and \(N\)) by exhaustively examining the performance of the ML-RCS with respect to the criteria pertinent to RCSs. We generated BER vs. MSNR\({}_{\text{dB}}\) graphs by utilizing the same ranges of \(\alpha\), \(\Delta\beta\), and \(N\) as those used for generating the confusion matrices. In Figure 9, we present a systematic BER analysis of the ML-RCS with respect to RCSs. The simulations comprised 10,000 iterations for a single BER data point at a given MSNR\({}_{\text{dB}}\). Stabilization was achieved at a tolerance of \(\pm\)0.01 for 8000 iterations to verify convergence.  
Figure 8: Left column: Confusion matrices when \(\Delta\beta\) = 1.6, 1.8, and 2. This figure shows the reduction in the error during the retrieval when \(\Delta\beta\) is increased. Middle column: Confusion matrices when \(\alpha\) = 0.6, 0.8, and 1.1. This figure shows the reduction in error during the retrieval when \(\alpha\) is increased. Right column: Confusion matrices when \(N\) = 15, 25, and 35. This figure shows the reduction in the error during the retrieval when \(N\) is increased.  
In Figure 9, the effects of increasing \(\alpha\) move horizontally for a given row, whereas the effects of increasing \(\Delta\beta\) move vertically for a given column. Moreover, every individual BER vs. MSNR\({}_{\text{dB}}\) graph shows the effects of increasing \(N\) for fixed values of \(\alpha\), and the lowest value of the MSNR is considered an ideal performance. The figures reveal that, similar to the results in the previous sections, the performance of the ML-RCS increases as \(\alpha\) increases for a fixed \(\Delta\beta\) and \(N\) in any given row. Moreover, increasing \(\Delta\beta\), for a fixed \(\alpha\) and \(N\), also has an incremental effect on the BER performance. For any individual BER vs. MSNR\({}_{\text{dB}}\) graph, the performance increases as \(N\) increases. The ideal BER performance is achieved at MSNR\({}_{\text{dB}}=-14\) for \(\alpha=1.1\), \(\Delta\beta=2\), and \(N=35\), which is a combination of the maximum values utilized for these parameters. This further confirms the trends found for \(\alpha\), \(\Delta\beta\), and \(N\) while exhaustively examining the performance of the ML-RCS, as observed in the previous section. Given the BER vs. MSNR\({}_{\text{dB}}\) results for the previously proposed RCSs [43; 44; 45; 46; 47; 48; 49; 50; 51; 52], the ideal performance of the ML-RCS is comparatively achieved against a very low value of the MSNR\({}_{\text{dB}}\). This further shows that the incorporation of pretrained DTs and an \(\alpha\)-SNS\(\mathcal{D}\) not only inherits the building security of an Sk\(\alpha\)SNSK-based RCS, but also outperforms the previously proposed models in terms of its BER performance.  
In this study, all the results for the ML-RCS were generated via an FT-based pretrained DT. However, we present a comparative BER vs. MSNR\({}_{\text{dB}}\) analysis with CT-based pretrained DTs and MT-based pretrained DTs in Figure 10. We present this to justify the selection of the FT-based pretrained DT for the ML-RCS against other potential configurations. For any common combination of \(\alpha\), \(\Delta\beta\), and \(N\), the ML-RCS achieved an ideal BER of \(10^{-3}\), which was achieved against the lowest value of the MSNR, while utilizing the FT configuration compared with its counterparts. Therefore, the FT-based pretrained DT was chosen to record all the results in the previous sections.  
Figure 9: BER vs. MSNR\({}_{\text{dB}}\) performance of ML-RCS; total transmitted bits = 1000. The figures show that the ML-RCS performance increases with an increment in the Sk\(\alpha\)SNSK responsible factors (\(\alpha\), \(\Delta\beta\), and \(N\)).