## 3 Methodology
### Transmission
#### 3.1.6 Link Failure Prediction  
This section deals with the use of RDBSCAN in the link failure prediction from \(\Omega\), and improvements in the security and prevention of point-to-point failure during transmission. Traditional methods for Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are known for finding intricate patterns in data that could indicate out-of-the-ordinary failure scenarios. However, in high-dimensional spaces, they encounter problems caused by the curse of dimensionality. The DBSCAN technique resolved this problem by utilizing a Radial Basis Function (RBF) kernel. In order to better model non-linear relationships, this function raises the data to a higher dimensional space. The primary points \(s\) and \(s^{\prime}\) were obtained from \(\Omega\). The next step was to use \(s\) and \(s^{\prime}\) in the calculation of the kernel of the Radial Basis Function. \[\widetilde{R}=exp\left[-\left[\frac{\|s-s\|^{2}}{2\partial^{2}}\right]\right] \tag{17}\]  
Here, \(exp\) signifies the exponential function and \(\partial\) signifies the bandwidth parameter. After that, from \(\widetilde{R}\), Epsilon \((\Phi)\) and MinPts \((M_{in})\) are computed. Then, from \((\Phi)\) and \((M_{in})\), the core points are formed and are expressed as follows:  
\[C_{p}=\{s,s^{\prime}e\Omega|\ d(s,s^{\prime})\leq\Phi\} \tag{18}\]  
\[C_{p}\geq M_{in} \tag{19}\]The performance of RDBSCAN in link failure prediction is influenced by the choice of two critical parameters: the RBF kernel bandwidth (\(\partial\)) and the minimum number of core points (\(M_{in}\)). The bandwidth parameter \(\partial\) in Equation (17) determines the spread of the radial basis function. A smaller \(\partial\) results in a narrower kernel, which may lead to overly tight clusters and a higher likelihood of identifying noise (false link failures). Conversely, a larger \(\partial\) may over-smooth the feature space and merge distinct failure patterns, potentially reducing sensitivity to localized anomalies. In this study, \(\partial\) was selected empirically based on cross-validation over a subset of the authenticated channel data \(\Omega\) to balance specificity and generalization. Similarly, the value of \(M_{in}\) controls the minimum density required to form a cluster. A higher \(M_{in}\) increases the robustness of cluster formation by requiring more neighbors to classify a point as a core point, which helps reduce false positives in sparse regions. However, overly high values may cause genuine failure clusters to be missed. Based on empirical testing, \(M_{in}\) was set to 5, which provided a good trade-off between accuracy and noise filtering in link failure prediction. Both parameters were fine-tuned to maximize the accuracy of prediction of failure versus non-failure links, evaluated using standard metrics such as precision, recall, and F1-score. Here, \(C_{p}\) signifies the core points and \((s,s^{\prime})\) embodies the distance between \(s\) and \(s^{\prime}\). Then, for each core point, a cluster \((\widetilde{l})\) is created and the density-reachable points are added recursively to \((\widetilde{l})\). This is expressed as follows:  
\[\widetilde{l}=\left\{ \begin{array}{l}C_{p}\end{array} \right\}\bigcup\left\{ \begin{array}{l}C_{p}|d(s,s^{\prime})\leq\;\Phi\;C_{p}\in M_{in}\end{array} \right\} \tag{20}\]  
Following the determination of \(C_{p}\) and, \((\widetilde{l})\) the final clusters \(Y\) predict the condition of the link in \(\Omega\). \[Y=\left\{ \begin{array}{l}Y^{\prime}\\ Y^{\prime\prime}\end{array} \right. \tag{21}\]  
Here, \((Y^{\prime})\) signifies the link failure and \(Y^{\prime\prime}\) signifies no link failure. The algorithm for the RDBSCAN approach is given below (Algorithm 1).