## 5 Experimental Environment
### Experimental Results and Discussion
#### 5.2.3 Security Analysis  
This section presents the security analysis of our proposed network. We conducted simultaneous transactions in the network, varying the number of workers and transaction loads to assess the network's susceptibility to DoS attacks. Specifically, we aimed to evaluate the risk of the order service becoming unavailable due to processing a large volume of transactions. These simulating conditions could be exploited in a DoS attack scenario. We checked the failure rate in the network under the given workload. Figure 9 illustrates the impact of varying the number of workers (or nodes) on the total number of failed transactions under a transaction load of 8000 TPS. Our testing involved deploying 50, 80, and 100 workers to initiate 8000 transactions simultaneously. The results indicate a concerning trend: as the number of workers increases, the network becomes more vulnerable to DoS attacks, resulting in a higher rate of transaction failures. In our subsequent test, we evaluated our network's performance under varying transaction loads ranging from 6000 TPS to 8000 TPS, with different numbers of workers. Figure 10 illustrates that our network demonstrated resilience when processing up to 6000 TPS across 50, 80, and 100 workers, achieving a 100% success rate. However, the network's stability was compromised as the transaction load increased to 6500 TPS. At this point, the network became susceptible to DoS attacks, resulting in network congestion and rendering the orderer service unavailable. Consequently, transaction failures began occurring once the transaction load reached 6500 TPS, underscoring the importance of optimizing the network capacity and implementing robust security measures to safeguard against potential disruptions and attacks at higher transaction volumes. Subsequently, we computed a correlation matrix (Table 5) to analyze the relationships between the variables (workers, total transactions, send rate, latency, throughput, failed transactions, and failure rate (%)) using the experimental data. The correlation matrix provides insight into the strength and direction of the associations between these key performance metrics and the behavior and interdependencies within our experimental network environment. The key findings of the correlation matrix are outlined below:  
* **Workers vs. Other Variables:** Workers have a negative correlation with the send rate (\(-0.49\)), throughput (\(-0.32\)), latency (\(-0.13\)), failed transactions (\(0.28\)), and failure rate (%) (\(0.30\)). More workers are associated with a lower send rate, throughput, and latency and a slightly higher failed transaction number and failure rate. * **Total Transactions vs. Other Variables:** The total transactions have a positive correlation with the failed transactions (\(0.89\)) and failure rate (%) (\(0.88\)) and a negative correlation with the send rate (\(-0.37\)) and throughput (\(-0.17\)). Higher total transactions are strongly associated with more failed transactions and a higher failure rate, showing weaker negative associations with the send rate and throughput. Figure 9: Transaction failure by varying worker nodes (transaction load = 8000). * **Send Rate vs. Other Variables:** The send rate has a negative correlation with the workers (\(-0.49\)), latency (\(-0.50\)), failure rate (\(\%\)) (\(-0.47\)), and a positive correlation with the throughput (\(0.46\)). A higher send rate is associated with fewer workers, lower latency, a lower failure rate, and higher throughput. * **Latency vs. Other Variables:** Latency has a negative correlation with the send rate (\(-0.50\)) and throughput (\(-0.51\)) and a positive correlation with the failed transactions (\(0.20\)) and failure rate (\(\%\)) (\(0.21\)). Lower latency is associated with a higher send rate, throughput, and slight increases in failed transactions and the failure rate.