## 3 Related Work on Protection against Sybil Attacks  
In a Sybil attack, the attacker creates a large number of false identities with which he can propagate messages to other network participants. This poses a serious security risk as the attacker can send false messages and simulate his location (the attacker may claim to be in multiple places at once) [35]. Due to the increased number of vehicles, it is possible to carry out a distributed denial-of-service (DDoS) attack by overloading other neighboring units [36]. Moreover, in voting-based systems, an attacker can easily outvote the real participants, leading to a decision in his favor. The Sybil attack presents a significant threat to VANET, as a successful vehicle could claim to have witnessed accident or traffic jam, which would alter the proposed route for any other vehicle. It could result in enormous costs for the network and disruptions to data continuity (Figure 3). \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Method** & **Advantages** & **Limitations** \\ \hline \multirow{2}{*}{[PERSON] et al. [17]} & \multirow{2}{*}{TRIP} & \multirow{2}{*}{Probabilistic reasoning identifies a node based on multiple factors.} & Difficult to preserve the node’s trust and behavior, as the system does not know that the node is truthful or malicious. \\ \cline{3-4}  & & & \\ \hline [PERSON] et al. [34] & \multirow{2}{*}{RABTM} & Dempster–Shafer evidence theory is used for numerical trust computation. & Probability can model all forms of uncertainty and ignorance. \\ \cline{3-4} [PERSON] et al. [14] & \multirow{2}{*}{CoE} & Source priorities could be adjusted by a particular event program in order to mitigate computation. & Require data taken from the multiple sensors in order to provide reliable results. \\ \cline{3-4} [PERSON] et al. [28] & \multirow{2}{*}{Post-crash warning} & \multirow{2}{*}{Effectively reduces the false positives and false negatives while effectively detecting misbehavior.} \\ \cline{3-4} [PERSON] et al. [31] & & & \\ \cline{3-4}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of current bogus prevention methods. The misbehavior detection schemes (MDS) method proposed in 2011 by [PERSON] in [37] was based on the detection of messages on the web and forged vehicles by examining the stream of reported information. Each entity has to decide whether the information received is true or not; the decisions are made based on consistency with other received messages. The algorithm does not use the voting system, so it is resistant to massive attacks in which the attacker gains numerical advantage [38]. If any inconsistencies are detected, lowering the priority of a given vehicle is used instead of revoking the certificates [39]. [PERSON] in [40] proposed a way to pluck incorrect information in VANET networks. It consists of examining the consistency of messages transmitted with the actions of the sender. Every vehicle sends its speed and position, and based on the real date, the behavior of that driver is computed [41]. Then, the system estimates whether the vehicle is sending the real information that matches the actual situation on the road. The speed information is verified against the vehicle's position. The simulations show that this approach effectively detects false information, but requires speedy processing to avoid time-barring events [42]. The method proposed by [PERSON] in [43] bases on rationalizing the location of vehicles based on readings from their GPS sensors. Since two vehicles cannot physically \"overlap\", the occurrence of such a situation triggers security mechanisms against attacks. The method does not require the use of additional hardware, which simplifies the car equipment infrastructure [44]. [PERSON] et al. [45] created his solution depending on the similarity of the motion trajectories of Sybil nodes, which are impractical and inappropriate in the real world, believing that Sybil nodes always have the same position and motion trajectories.