## 5 Experiments
### Experiment 3: Distribution and Recovery Time of Malicious Connections  
This experiment provides a triplet of additional key remarks derived from the experiment of Section 5.2. First, we demonstrate that all the static splitting factor strategies uphold the normal distribution throughout the simulation. Second, we prove that resource utilization grows as the splitting factor increases. Third, we show that the proposed scheme leads to high resilience; this is confirmed by the relatively brief time required for the system to recover. We opt to visually confirm these observations through the best time- and resource-wise performers as derived from the experiments of Section 5.2, namely, the splitting factors of 10 and 2, respectively. Particularly, the first observation is illustrated in Figures 5 and 6, the second in Figures 7 and 8, while the third one in Figure 9.  
Specifically, Figures 5 and 6 depict the distribution of connections across replica nodes. The \(x\)-axis denotes time in epochs, and the y-axis represents the number of connections each node includes. Each vertical bar in the figure corresponds to a node. The red and green portions of each node indicate the malicious and healthy connection percentages, respectively. Note that both figures depict the condition at the end of the epochs, namely, after the evaluation has been completed and the healthy nodes have been discarded. Put simply, all the depicted nodes contain malicious connections. Indicatively, both Figures 5 and 6 pertain to the scenario where the total number of connections was 10K and the pollution factor was 10%, while the employed strategies include the ones with splitting factors of 2 and 10, respectively. Epochs 4 to 8 in Figure 5 have been selected to  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Num. of connections** & **SF = 2** & **SF = 4** & **SF = 6** & **SF = 8** & **SF = 10** & **SF = 1  
provide a balanced representation for the case of a splitting factor of 2. This decision was based on the fact that the rightmost epochs (5 to 14) encompass a vast number of nodes, whereas the leftmost epochs (1 to 3) involve significantly fewer nodes, offering less distinct and visible information to the reader. In contrast, in the case of a splitting factor of 10, the simulation only takes 4 epochs, so we chose to depict epochs 2 to 4 in Figure 6. The first epoch was deliberately skipped as the number of nodes is significantly smaller and the connections per node are significantly more than the subsequent epochs, upsetting the clarity of Figure 6.  
Figure 5: Snapshot of the connections’ distribution over simulation epochs. This case considers a connections’ pool of 10K, a pollution factor of 10%, and a splitting factor of 2. In the parenthesis are the total nodes for that epoch. Figure 6: Snapshot of the connections’ distribution over simulation epochs. This case considers a connections’ pool of 10K, a pollution factor of 10%, and a splitting factor of 10. In the parenthesis are the nodes that exist at the end of each epoch. Figure 7: Snapshot of the distribution of malicious connections over replicas in selected epochs. This case considers a connections’ pool of 10K, a pollution factor of 10%, and a splitting factor of 2. **(a)** Epoch 6 out of 14; **(b)** Epoch 8 out of 14. Observing Figures 5 and 6, it is evident that the assumed normal distribution at the start of the experiments is maintained over time. Noteworthy, this desirable property is retained by our partition strategy, irrespective of which of the strategies of Section 5.2 is employed, as confirmed by Figures 5 and 6. Note that, as the number of nodes increases, the dispersion of malicious connections across the nodes widens. It is also straightforward that as the splitting factor increases this dispersion process is accelerated. This is because the partitioning process is applied uniformly to all nodes, irrespective of their maliciousness.