## 5 Results and Analysis for Sybil/Bogus Prevention Algorithms
### Bogus & Sybil Trust Level & Timestamp (B&STL&T) Algorithm  
Table 3 shows the results obtained via the first algorithms that focus on trust level and timestamp only mentioned in [6]. The first column refers to the experiment number. Then, the next two ones refer to the number of user and number of attackers inside the network. Finally, the fourth column shows the ratio between ordinary drivers on the road and attackers.  
Table 4 presents the ratio between attackers and attackers and the time needed to identify the first attacker. In the experiments, the ratio between the attackers and the victim  
Figure 9: Main view of the application of VANET network to mock the road environment.  
nodes vary from 9% up to 100% accordingly. The maximum number of users reached 200 vehicles on the road. The average time needed to identify the first attacker was always 29 ms, and the standard deviation was \(\pm\)15%.  
Table 5 shows how much time the system needs to identify all attackers. Finally, Table 6 shows false positives.  
The first series of conducted experiments ratio between victim nodes and attackers varied from 10% up to 100%. The maximum number of users reached 200 vehicles on the road. The average time needed to identify the first attacker was 29 ms, and the standard deviation was \(\pm\)15%. The time needed to identify all attackers increase proportionally with the increasing attackers' ratio against victim nodes. The network with the increasing number of computations gathers more data, which simplifies the further computations. Therefore, increasing the number of vehicles up to 175 (75 attackers) leads to a detection time around 116 ms. In the scenario with having 15 cars in the network (and 5 attackers), computation was ended (removing all attackers from the network) within 78 ms. Increasing the number of vehicles by a factor of 11 increased the computation time by a factor of 2.2. The number of false positives (incorrectly marked vehicles as attacker nodes) was always 0 or in some extreme cases 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No Experiment** & **Number of Users** & **Number of Attackers** & **Ratio** \\ \hline
1 & 10 & 1 & 9.09\% \\ \hline
2 & 10 & 5 & 33.33\% \\ \hline
3 & 10 & 10 & 50.00\% \\ \hline
4 & 50 & 1 & 1.96\% \\ \hline
5 & 50 & 5 & 9.09\% \\ \hline
6 & 50  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No Experiment** & **Ratio** & **Time to Identify First Attacker [ms]** \\ \hline  
\hline \end{tabular}
\end{table}
Table 4: Bogus & Sybil Trust Level & Timestamp algorithm—time to identify the first attacker in ms.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No Experiment** & **Ratio** & **Time Needed to Identify All Attackers [ms]** \\ \hline
1 & 9.09\% & 48 \\ \hline
2 & 33.33\% & 78 \\ \hline
3 & 50.00\% & 76 \\ \hline
4 & 1.96\% & 35 \\ \hline
5 & 9.09\% & 46 \\ \hline
6 & 33.33\% & 121 \\ \hline
7 & 50.00\% & - \\ \hline
8 & 16.67\% & 142 \\ \hline
9 & 37.50\% & 186 \\ \hline
10 & 42.86\% & 116 \\ \hline
11 & 50.00\% & 180 (without the last two) \\ \hline
12 & 100.00\% & 58 \\ \hline
13 & 100.00\% & 103 \\ \hline
14 & 100.00\% & 143 (to penultimate) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Bogus & Sybil Trust Level & Timestamp algorithm—time needed to identify all attackers in ms.