## 4 Prevention Mechanisms of the Proposed Scheme
### _RSU Network Prevention Mechanism Against Hybrid DoS Attacks_  
The network construction is done with the specifications given in Table 3. Algorithm 7 shows the random vehicle positioning (Total Vehicles). Algorithm 8 shows random End-to-end delay detection in Vehicles. ```
1 for i = 1: Vehicle/Nodes//Loop running for each node
2 set End2 End Delay_n (i) = Random;//Putting an end-to-end delay value for node acting normal
3 End2 End Delay (i) = \((\)_End2 End\(\)_Dealy_n\()^{2}\);//now, the expected reality is unpredictable and hence just for the random//architecture is set to be square of the normal delay
4 endfor
5 end \begin{table}
\begin{tabular}{c c} \hline Total Number of Vehicles & 60–100 \\ The height of the network & 1000 m \\ The width of the network & 1000 m \\ Node displacement & 150–500 m/s \\ Simulation iterations & 1500 \\ Simulation tool & Matlab \\ Encryption technique & Vehicular rsa \\ mac/phy & IEEE 802.11p \\ \hline \end{tabular}
\end{table} 
Table 3: Network Specifications. As the end-to-end delay is initialized, in a similar fashion the other parameters, such as jitter, packet drop, jamming signal resource consumption (RSU/CPU) overutilization, and all other forms of anticipated attacks, including HDSA and DoS JSA attacks, in the network performance metric parameters are also initialized. In VANET, we envisage that there will be no excessive battery consumption. This is due to the fact that as the vehicles that are in the communication process keep moving in order to determine the end-to-end delay of the network, the battery also keeps charging as long as the vehicle are running. In addition, every node has a different set of parameters. A function is designed to initiate network parameters. The real-time simulation may have a slightly different structure. Networks do not have any fixed structure, nevertheless, for any simulation there are parameters that should be initialized. ### Modelling of DoS Threat Prevention

This paper focuses on the prevention of all forms of attacks, including HDSA and DoS JSA attacks. The architecture for the attacks are as follows. Figure 6(a),b represents the path construction and the malicious network for HDSA, including DoS JSA and other attack modes of the attackers, respectively. Figure 6(b) shows that the intensity of dumping end-to-end delayed packets of the HDSA, including DoS JSA attackers such as jamming of signals, packet drop, and resources consumption via CPU/RSU overutilization, varies at different instances of time. If the intensity of all these forms of hybrid DoS attackers and others are high, obviously the attackers attempt to dump more packets, which results in more packet drop, jamming of signals, and resources consumption via RSU/CPU overutilization, etc., which might affect the RSU for prolonged end-to-end delay in the network. Based upon this, we define the following equation:

\[\text{Tpd}=\text{Pdn}+\text{Pda} \tag{16}\]

Tpd is total packet drop, Pdn is the total number of dropped packets in normal mode, and Pda is the packet dropped when the network is under threat, which has experienced all types of DoS attacks. In addition, we also define the following equation in relation to the types of attacks as:

\[\text{Pdr}=(\text{Tp}-\text{Tpd})/\text{Tp} \tag{17}\]

Pdr is packet delivery ratio and Tp is the total number of packets. Due to random behavior of the attacks, the PSAM becomes more sophisticated. Now, the challenge is to identify all the forms of DoS

Figure 7: (a) Path Constructed; (b) malicious node HDSA and DoS JSA attackers. attacks that are experienced in the network. The proposed scheme solution utilizes FFBP-NN and the general functions of FFBP-NN and artificial intelligence are given in Table 4.

The artificial intelligence used in the proposed scheme consist of two methods: (1) Training, and (2) Classification/Optimization. The proposed scheme models, which include HDAM, PSAM, and the PESATRM, utilize two processes in artificial intelligence (AI). They are the training process and the classification/optimization process.