# An Improved Machine Learning Model with Hybrid Technique in VANET for Robust Communication
## 6 Results and Discussions
### Dataset Description and Evaluation Metrics
#### 6.1.2 Performance Analysis  
The performance of proposed approaches is described in this part using measures such as energy consumption, drop, delay, throughput, packet delivery ratio, and fairness index. (1) **PHASE 1:**  
Phase I detected the DDoS attack using a novel machine learning technique called SVM-HHO, which provided vehicle information. This section describes the analysis, and the proposed performance is detailed below. Performance measurements were primarily used to assess the model's ability to identify DDoS attacks and allocate bandwidth in VANET situations. From Table 3, it is shown that the proposed modified machine learning-based hybrid optimization technique is more effective compared with existing approaches. In node 60, the proposed methodology attained energy consumption of 0.35 J, delay ratio of 50, and drop ratio of 14.6785. In addition, the suggested approach attained an efficiency of 1710 in comparison with the existing method that attained efficiency of 794. * Consumption of Energy  
The total energy consumption of the proposed approach is shown in Figure 8, based on the data in Table 3. The rate of energy consumption of each node is noted. At node 100, the energy consumption of 0.5 is habited. The increase in nodes is accompanied with an increase in energy consumption. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Node**} & \multirow{2}{*}{\
\begin{tabular}{c} **Energy Consumption** \\ **(Joules)** \\ \end{tabular} } & \multirow{2}{*}{**Drop**} & \multirow{2}{*}{**Delay**} & \multicolumn{2}{c}{**Throughput (kilo Bytes/sec)**} & \multicolumn{1}{c}{**Network**} & \multirow{2}{*}{
\begin{tabular}{c} **Packet Delivery** \\ **Lifetime** \\ \end{tabular} } & \multicolumn{1}{c}{**Fairness**} \\  & & & & & **Ratio (PDR)** & \\ \hline \multicolumn{8}{c}{**Proposed Method**} \\ \hline
20 & 0.25 & 1.6531 & 97 & 1278 & 339.52 & 20 & 4.2477 \\ \hline
40 & 0.32 & 9.9854 & 37 & 1920 & 664.32 & 33 & 2.4125 \\ \hline
60 & 0.35 & 14.6785 & 50 & 1710 & 524.00 & 48 & 1.7961 \\ \hline
80 & 0.43 & 50.5456 & 95 & 1345 & 370.81 & 59 & 1.5752 \\ \hline
100 & 0.51 & 57.1534 & 68 & 1209 & 307.71 & 64 & 1.3947 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of proposed approach. Figure 9 shows the data rate-based energy consumption ratio of the suggested approach. If the data packet rates are high, the energy is at a peak; if not, the energy produces lower data rates. From Table 4, we can see that at node 20, energy consumption is 0.8, obtained at a data rate of 4, 1.2 is attained at a data rate of 6, 1.6 is acquired in a data rate of 8, and 2.1 is obtained at a data rate of 10. At a data rate of 12, 2.5 is gained, and at a data rate of 14, energy consumption is 2.9. Similarly, in node 40, energy consumption is 0.4 at a data rate of 4, 0.6 at a data rate of 6, 0.8 at a data rate of 8, 1.1 at a data rate of 10, 1.2 at a data rate of 12, and 1.5 at a data rate of 14. Moreover, at node 60, energy consumption is 0.5 at a data rate of 4, 0.8 at a data rate of 6, 1.0 at a data rate of 8, 1.2 at a data rate of 10, 1.6 at a data rate of 12, and 1.8 at a data rate of 14. In addition, at node 80, energy consumption is 0.7 at a data rate of 4, 1.1 at a data rate of 6, 1.5 at a data rate of 8, 1.9 at a data rate of 10, 2.3 at a data rate of 12, and 2.6 at a data rate of 14. Furthermore, at node 100, energy consumption is 0.8 at a data rate of 4, 1.1 at a data rate of 6, 1.5 at a data rate of 10, 2.3 at a data rate of 12, and 2.6 at a data rate of 14. Furthermore, at node 100, energy consumption is 0.8 at a data rate of 10, 2.3 at a data rate of 12, and 2.6 at a data rate of 14.