## 4 Experiment
### Results and Analysis
#### 4.4.4 PDSSS System Analysis
The primary reason we use this data is that it has the properties of a peer-to-peer network presented in the models. Additionally, the higher clustering coefficient and higher number of triangles in the network allows us to easily extract smaller subgraphs to perform simplified versions of the simulations. Moreover, data contains a 'ground-truth' community membership of the nodes. Each node can only belong to one of the 42 departments. This makes it easy for us to classify nodes into high-value nodes and low-value nodes based on the department number. In addition to this, email services are often the victims of real-world DDOS attacks, which makes the dataset a relevant representation of the network model. The following tables detail the dataset. Figure 10: GUI (Graphical User Interface) layout of the Pareto Defense Strategy Selection Simulator (PDSSS) system. Note that the dataset does not contain all the necessary information for the models in the simulations. The missing configuration data has been auto-generated. The PDSS system overview is shown in Figure 11, which is under the configuration of Table 6, and the user can select the regular network graph definition features: the number of nodes and server-client ratio. The user can select the model specific graph definition features: edge sparsity and cost ranges for bandwidth and CPU resources. The user can select the model features: attack resources and the scalarization ratios for the attacker and defender. The number of possible strategies is determined by the model. This model has a 3-D solution space. Figure 11: PDSS system overview. (**a**) Control panel; (**b**) Results display. \begin{table}
\begin{tabular}{c c} \hline \hline
**Nodes** & **1005** \\ \hline Edges & 25,571 \\ Average clustering coefficient & 0.3994 \\ Number of triangles & 105,461 \\ Fraction of closed triangles & 0.1085 \\ Diameter (longest shortest path) & 7 \\
90-percentile effective diameter & 2.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Dataset statistics. In the first graph in Figure 11, it is difficult to visually identify the best strategies from the initial graph as most of the colors are spread across the graph. However, there is a noticeable concentration at the top-right corner of the graph. On the second graph, the optimal values are much easier to interpret. The orange (~600, ~950, ~650) and blue points are much more vivid than the khaki points, indicating higher scores in the goal functions. The orange and blue points are diagonally oriented from the inner upper left corner to the outer bottom right corner. This explains why these colors are brighter at the center. The other optimal values scattered near the walls are faint, indicating they score very low in one of the goals, although they are in the optimal set. Figure 12 shows the PDSS system with a more realistic network size with configuration in Table 7. As is visible from the image, the graphical representation of the results is no longer useful. The algorithm also took much longer to graph the results. For the actual running of the experiments (which includes many repetitions), we opted not to use the GUI and instead ran the code from the command line. In the first graph in the above figure, the initial strategies are cramped into the plot making it nearly impossible make an inference solely from the graph. However, the second graph shows the optimal non-dominated strategies. It is much easier for a DM to make a decision in this case just by referring to the second graph: all of the unnecessary dominated strategies have been removed. It seems green is the most distributed strategy, while blue and orange have fewer options.