## 4 Prevention Mechanisms of the Proposed Scheme
### _RSU Network Prevention Mechanism Against Hybrid DoS Attacks_
In the training process we utilized jitter as the training parameter to train the neural network using the MATLAB neural network tool box. Based upon the training process, a target set is provided as well. The training is orchestrated in two phases. In the initial phase, the training is done for path identification of all vehicles paths that were affected by HDSA, including DoS JSA and other attacks, based upon the communication process experience of vehicles through the transmission of the IEEE 802.11p beacon relay. Then, in the second process, the training is done to identify the vehicles on the route that were also affected by HDSA, including DoS JSA and other attacks. The classification/optimization process optimizes the real-time signal timings during a given attack situation. These would incur HDSAs, including DoS JSA and other attack traffic, which would result in congestion or jamming of signals, packet drop, and resource consumption via RSU overutilization. Equation (18) below can be defined by the end-to-end jitter based upon AI processes, as follows:

\[\text{Jitr}=\text{E2 EDP}\left(\text{at, nt}\right)+\text{Ntd} \tag{18}\]

From Equation (18), Jitr is the jitter, E2 EDP is the end-to-end delay of the path, \"at\" and \"nt\" represent advanced (under threat) and normal, respectively. Ntd is the network delay. For each path in every iteration, there will be a jitter. The proposed solution uses the first 450 to 600 iteration data points for training, and then for the next 650 iterations and above to train the structure for identification of the path delayed in the vehicle communication process based upon the proposed scheme and models. Algorithm 9 shows Train_Neural (Reiteration Data, Total Reiterations). The used notations are as follows: \begin{table}
\begin{tabular}{c c} \hline \hline Total Hidden Layer & 1 \\ Neuron Count & 40 \\ Feeding Iteration & 140 \\ Reverse Iteration & 30–60 \\ PropoFireflytion Type & Linear \\ Algebraic Model & Levenberg \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Firely used Feed forward back propagation neural network (FFBP-NN) structure. The training section leads into the following (Firefly/GA) FFBP-NN structure. Figure 8a illustrates Feed forward propagation structure and Figure 8b illustrates the back propagation Firefly graph. Based upon the graph, the proposed scheme and the models determine the training data jitter and also validate it. The training data jitter also represents the deviation between predicted y value and also the actual y value, which is the measured MSE (mean square error). In addition, based upon Figure 8b, we can also realize that we have 9 epochs of the proposed scheme model. This implies that the proposed scheme models are trained over 9 epochs as the forward iteration and 3 epochs for backwards iteration. We also expect that the proposed scheme models will also decrease with each epoch, meaning our model predicts value y more frequently and accurately as the model is further continued for training. The test graph also indicates that validation performance at epoch 3 prediction of the proposed models is a good one. ### Identification of All Affected Nodes and Retrieval

The proposed research work scheme also presents a regression model with the back propagation, as shown in Figure 9.

Figure 8: (**a**) Feed forward propagation structure; (**b**) Firefly back Propagation. ## 5 Analysis and Discussion of Results

From Figure 9, the regression model of the proposed scheme is evaluated. Based upon the evaluation, the training result is 0.7847, the validation result is NaN (not a number), the test result is NaN, and the overall result is 0.98727. These values represent close but high regression values. Generally, close but high regression values represents healthy training and classification structures. High regression values are also the reason that the prevention parameters of HDSA are high, including DoS JSA and other attack activities causing jitter and delay in the network.