## 6 Discussion and Conclusions  
The artificial VANET network (mentioned in Section 4) allows its users to share their information on onroad events and traffic. While this knowledge sharing mechanism (trans-ferring messages between OBUs (onboard units) located inside vehicles) is constructive, it may also be the target of security attacks. There are different ways of misinforming people and different motives for it. As any engineer should be prepared for his device to be targeted, each user should be conscious of the risks of any technology. Via a VANET network or just peer-to-peer networks, several ways to spread disinformation have been determined and some of those mechanisms have been prepared against two forms of attack: Sybil and bogus.  
After conducting a series of experiments, a certain phenomenon was noticed in the case of situations in which the ratio of attackers to all vehicles in the network was greater  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No Experiment**} & \multicolumn{2}{c}{**False Pos. after**} & \multirow{2}{*}{\
\begin{tabular}{c} **Time to All** \\ **Detection** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **No. of Ordinary** \\ **Vehicle** \\ \end{tabular} } & \multirow{2}{*}{**No. of Attackers**} & \multicolumn{2}{c}{**Attacker to**} & \multirow{2}{*}{**Method**} \\  & **Attack.** & & & & & \\ \hline  
\hline \end{tabular}
\end{table}
Table 8: Results obtained by the authors of [19; 32; 34; 37; 40; 41; 43; 45; 46]; the research works examined papers in order to conduct the same experiments that were tested and presented in Table 7.  
than or equal to 50%; then, the network was able to classify all attackers inside the network correctly. Moreover, after code improvements and analyzing the results from Table 6--column \"Time needed to identify all attackers (ms)\" with results from Table 7--column \"Time to all detection\", there is a significant change in the time of detecting all attackers; this operation is being performed much faster.  
Moreover, after implementing changes to the application of artificial VANET network (mentioned in Section 4), previously described the phenomenon of a single attacker not being properly marked disappeared. Cause of this event might be interference of the code changes to the speed of cars. Previously, every car in the network traveled with static speed. Currently, the speed of every car adapts to events on the road.  
In cases where the number of ordinary vehicles was lower than the number of attackers, the detection of the first attacker decreased significantly. The time of detection of the first attacker lengthened only in experiments 12-14, where the number of attackers was higher than the number of ordinary vehicles. The time of detecting all attackers was reduced by approximately one-third.  
However, after analyzing and comparing the results from Table 6 with results from Table 7, it could be seen that after implementing changes to the algorithm, the number of false positives per run increased. Therefore, there is a potential place that should be improved in future work.  
The future of the ITS (intelligent transportation system), where safety and the attack-free environment is required to achieve the desired traffic quality, is the vehicular ad hoc network. However, it is exposed to different attacks due to the open nature of the VANET. The primary objective of this type of network is not only to provide road safety and operation but also to preserve the safety of all network nodes. Securing the VANET is thus a significant challenge. Privacy and authentication seem to be major obstacles with this type of networks. Any assault could represent a significant threat to our life.  
Conceptualization, K.S., A.P.-M.; Investigation, K.S.; Methodology, K.S.; Validation, A.P.-M. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.