## 3 Methodology
### Performance Parameters  
In this study, various parameters were utilized to assess the benchmarking analysis. The algorithms' accuracy, F1-score, precision, and recall were evaluated within the benchmarking scenarios. These metrics are crucial for evaluating classification tasks as they offer diverse viewpoints on the model's performance. Each metric provides unique insights into the model's predictive abilities, aiding in evaluating its efficacy in addressing classification challenges. Furthermore, this research measured the training model's size generated by each algorithm and the duration of both training and testing procedures. These metrics are pivotal for the practical implementation and performance of classification models, impacting resource allocation, costs, latency, scalability, and user experience. Striking a balance between model size, training duration, and prediction time is essential to ensure the development of efficient, effective, and feasible solutions for classification tasks in real-world applications. 1. **Accuracy (%)**: The accuracy of an IDS is crucial for effectively identifying and responding to malicious activity while minimizing false positives. High accuracy indicates correct identification of true positives and true negatives. Accuracy is computed as in Formula (7) [31]. \[\frac{TP+TN}{TP+FP+TN+FN}=Accuracy\] (7) where \(TP\) is true positive, TN is true negative, FP is false positive, and FN is false negative. 2. **Precision (%)**: Precision in IDS is the ratio of true-positive predictions to the total positive predictions made by the model, measuring the accuracy of positive predictions. It calculates the proportion of correctly predicted positive instances out of all instances predicted as positive. Precision is computed as in Formula (8) [31]. \[\frac{TP}{TP+FP}=Precision\] (8)
3. **Recall (%)**: The recall metric, also known as sensitivity or the true-positive rate, is the ratio of true-positive predictions to the total actual positive instances in the dataset. It gauges the model's ability to correctly identify positive instances, regardless of some instances being wrongly predicted as negative. Recall is computed as in Formula (9) [31]. \[\frac{TP}{TP+FN}=Recall\] (9)
4. **F1-Score (%)**: The F1-score, a widely used metric in machine learning for classification tasks, combines precision and recall into a single value to measure the model's accuracy. It offers a balanced evaluation of the model's performance, especially when false positives and false negatives are equally significant. F1 is computed as in Formula (10) [31]. \[\frac{2*(Precision*Recall)}{Precision+Recall}=F1-Score(F1)\] (10)
5. **TPS**: Measuring TPS for DLT involves assessing the system's transaction processing rate within a one-second time frame, serving as a crucial metric for evaluating a DLT's performance and scalability. TPS is determined by tracking the successful recording and confirmation of transactions on the ledger, reflecting the system's efficiency and capacity to accommodate a growing number of participants and transactions without compromising speed and reliability. This metric is influenced by factors such as the DLT's technology, consensus mechanism, network capabilities, and data-handling processes. In terms of hardware resources, TPS is closely tied to both CPU and RAM. The CPU is responsible for processing and validating incoming transactions, while RAM aids in efficient data storage and retrieval. An effective combination of CPU and RAM resources is vital for achieving and sustaining high TPS rates in a DLT system, as they contribute to accelerated transaction processing and reduced latency. The formula for calculating TPS can be expressed as Formula (11) [32]. \[TPS=\frac{Total\ Transactions}{Time\ Period}\] (11)  
where \(TPS\) is the transactions per second, _Total Transactions_ represents the total number of transactions processed during the chosen time period, and _Time Period_ is the duration over which the transactions are measured in seconds. 6.