# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 5 Experiments and Analysis
### Comparative Analysis  
The studied performance was compared to prior work presented by [PERSON] al. [40] and [PERSON] et al. [13] to prove the efficiency of the constructed secure MANET against BHA utilizing the offered hybrid classification technique. The proposed work's average PDR, throughput (Kbps), and delay (ms) were compared to the average PDR, throughput, and delay of existing works, as shown in Table 8.  
With [13; 34], a comparative study of the planned work with previous work was carried out. In comparison to previous studies, it was discovered that the created hybrid AI strategy (SVM plus ANN) performed better. This is because the authors utilized a classic technique known as the CDS (Connected Discriminant Set) approach in [34], in which data transmission was done based on only one feature of nodes, namely the energy spent by nodes. The suggested study, on the other hand, employed an intelligent technique in which ANN and SVM are trained utilizing two separate features of nodes, such as energy consumption and data transmission latency. According to previous research [34], the risks of packet drop rise as each node retains a data packet for an extended period. To address this issue, we developed a BHA detection model that uses Artificial Intelligence (AI) to identify network intrusions automatically, with average PDR, throughput, and latency of 97.96 percent, 92.78 kbps, and 0.04 s, respectively. The reliability factor technique was employed by the authors in [13], in which the reliability factor of the nodes was determined, and if the computed value was high, the data were sent; otherwise, the data were rechecked for malicious behavior using the FRREQ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**PDR (\%)**} & \multicolumn{2}{c}{**Throughput (Kbps)**} & \multirow{2}{*}{**Delay (s)**} \\  & & **(Kbps)** & & \\ \hline
**Before** & **Proposed** & **Before** & **Proposed** & **Before** & **Provement** & **Proposed** \\
**Prevention** & & & & & \\ \hline
59.07 & 97.96 & 61.38 & 92.78 & 0.2304 & 0.04 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Statistical test values. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**PDR (\%)**} & \multicolumn{3}{c}{**Throughput (Kbps)**} & \multirow{2}{*}{**Delay (s)**} \\ \hline Proposed & [34] & [13] & Proposed & [34] & Proposed & [34] & [13] \\ \hline
97.96 & 96.94 & 95.14 & 92.78 & 79.08 & 0.04 & 0.087 & 0.035 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparative parametric value. message. If a node is malicious, it will respond to the FRREQ and be labeled as such. The procedure is time-consuming and yields a low PDR rate. As a result, to improve the work we used an optimization strategy using hybrid classification approaches to improve AODV routing and were able to achieve higher network performance in terms of PDR, throughput, and latency. The results comparison is described later. The comparison of PDR against the existing work performed by [13; 40] is depicted in Table 7. There is an improvement of 1.29% and 3.21%, respectively, against the work in [13; 34]. This is due to the appropriate selection of nodes while creating routes among source and destination nodes. The existing approach [34] worked on the energy consumption of nodes properties whereas in [13] the nodes were identified using a reliability score. In the proposed work, three nodes properties were considered for the identification of BHA nodes and therefore performed better compared to existing approaches. The comparative analysis for throughput analyzed against [34] work is also depicted in Table 7. The percentage increase in the throughput was calculated and the improvement of \(\left(\frac{92.78-79.08}{79.08}\right)\times 100=17.32\%\) was examined compared to the work in [34]. In comparison to prior work by [13; 34], the average delay measured for the proposed work was 0.087 ms and 0.035 ms, respectively.