## 2 Related Work on Protection against Bogus Attacks  
One of the VANET network attacks that involves sending false information to obtain personal benefits is a bogus attack. The attacker could be an outside (i.e., intruder) or an inside (i.e., legitimate) user in this assault [7]. In order to manipulate the behavior of other vehicles, the intruder broadcasts false information in the vehicular network by disseminating false network data. For instance, to prevent any vehicle from selecting a certain route, a vehicle may mimic heavy traffic on one road (Figure 2) [8]. As an example, Figure 2 demonstrates the real congestion situation where victim cars propagate the real behavior of the current situation. It shows the false congestion alert propagated by the attacker node, although other vehicles display no congestion messages. Therefore, to identify and avoid these false messages, the particular mechanism has to be implemented. The attacker chooses a victim node in the attack scenario and then prepares an RREQ (route request) or beacon packet for the victim's generated AODV (ad hoc on-demand distance vector protocol) and GPSR (greedy perimeter stateless routing protocol) [9]. The attacker generates packets for a randomly selected target victim node and broadcasts these packets every five seconds on behalf of the victim car. The attacker absorbs traffic and drops any packets transmitted through him by being the newest node or the nearest node to the destination in both AODV and GPSR. This attack may also be used to isolate a node from the network, but due to the rapidly changing topology of VANETs, it will have a limited impact [10]. Several solutions provide protection against bogus attacks. The most popular are:  
* Asymmetric cryptography--there is one public key in the entire network and each device has its own private key with which it signs its messages [11]. * Trust level--attempts to detect a bogus attack on the simulation network is based on assigning and modifying a particular vehicle's level. This level is used to recognize the truthfulness of the message sent by the vehicle [12]. All vehicles are assigned to a confidence level, which is verified when determining the correctness of the obtained information [13]. [PERSON] et al. [14] suggest a message filtering model to detect the misleading info efficiently. The model contains a threshold curve and occurrence certainty (CoE) curve. The CoE, which shows the trust level of the request data, is determined by integrating data from different sources, such as local sensors, RSUs, and reputing mechanisms [15]. A particular event program could adjust source priorities in order to mitigate computation. For instance, a road condition alert from a nearby trustworthy RSU might bypass other sources and make it impossible for them to be reviewed. The more a vehicle encounters a specific incident, the moretifications it receives to record an occurrence that raises the CoE value [16]. One of the solutions mentioned in [17] was based on the TRIP (trust-and-reputation-infrastructure-based proposal). This algorithm for traffic analysis identifies the malicious nodes in the VANET network that spread false or altered data through the network. Alert messages and regular messages are sent to another node that tests the reputation and credibility of the sender's node [18]. The received messages are dropped if the node turns out to be malicious. Probabilistic reasoning identifies a node based on the value derived from three pieces of information [19]: past credibility rating, cars surrounding it, and the central body's guidelines. The system proposed three values for estimating confidence values: untrusted (rejects all packet from the given node), trustworthy (accept without sending packets), and reliable (accept and send all information) [20]. Figure 2: Example of bogus information attack.