# Robust and Secure Data Transmission Using Artificial Intelligence Techniques in Ad-Hoc Networks
## 5 Experiments and Analysis  
### Results  
The results were computed in a MATLAB simulator that offers a simple platform for network simulation. The nodes were deployed in range of {N = 100, 200, 400, 600, 800, 1000, 2000]. The results were computed based on parameters such as throughput, PDR, and Delay (end-to-end). The obtained outcomes were examined using AODV along with ABC and the ANN approach [43]. Every performance parameter was performed with 100 iterations to achieve the best value. Comparative analyses were also performed to show the efficiency of the designed network. The performance of the designed black hole detection system was analyzed by evaluating parameters like PDR, throughput, and delay. 1. Packet Delivery Ratio (PDR)  
This parameter is defined as the rate of data packets gained through the target node that are produced by the sources. Mathematically, PDR is represented by Equation (7). \[\text{PDR}=\frac{\text{Number of Packet receive}}{\text{total Number of packet}} \tag{7}\]  
PDR has an important role, and it shows the actual number of information carriers received by the receiver. The greater the value of PDR, the lesser amount of error inside the network. 2. Throughput  
The parameter throughput is described in MANET through the successful delivery of the message or delivery of packets throughout a communication network. Typically, throughput is estimated in bit/s or bps. Mathematically, it is given by Equation (8). \[\text{Throughput}(\text{Th})=\frac{\text{Number ofpacket receive}}{\text{Total time interval}} \tag{8}\]  
3. Delay  
The delay parameter was defined as the ratio of the total length of time it takes packets to travel from the source to the destination node divided by the total number of data count packets. A total of 1000 packets started the data transmission. This is represented mathematically by Equation (9). \[\text{Delay}=\frac{\text{Delayed packets received at the destination}}{\text{total count of packets}} \tag{9}\]  
Table 4 lists the computed values for the planned network based on the PDR measure, and Figure 13 depicts the graph. PDR is the ratio of the total number of packets received to the total number of packets transmitted. The graph shows four different values. * Threat to Ad-hoc On-Demand Distance Vector (AODV)
* AODV with Threat
* AODV with Artificial Bee Colony (ABC)
* Artificial Neural Network (ANN) and Support Vector Machine (SVM) with AODV (after prevention)
* Using Decision Tree (DT)
* Using Random Forest (RF)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **Under** & **AODV** & **AODV** & **AODV** & **After Pre-** & **Using DT** & **Using RF** \\
**of Nodes** & **Threat** & **Without** & **With ABC** & **with ABC** & **with** & **With** & **With** \\ \hline
100 & 42. As per the given graph shown in Figure 13, it is clear that the proposed approach (modified AODV) using ABC and ANN with the SVM method performed well. The values were analyzed concerning the number of nodes as indicated on the x-axis. To show the enhancement comparison, other considered scenarios were presented. The average value analyzed with AODV under threat, AODV without threat, AODV with ABC, after prevention, using Decision Tree (DT), and using Random Forest (RF) was observed as 59.07, 64.94, 89.67, 97.96, 96.08, and 95.12, respectively. Therefore, the highest PDR was examined for the proposed work. This is because of the dual ML algorithm that improves the attack detection rate and hence improves the Quality of Service (QoS) parameter. Throughput represents the transmission information of packets in a network. The analyzed values for four different scenarios with different numbers of nodes 100, 200, 400, 600, 8  
The suggested job has a larger throughput than the other five cases: AODV under Threat, AODV with Threat, AODV with ABC, utilizing DT, and using RF. When the BHA occurs in the network, the system's performance suffers. To address this issue, AODV is utilized as a routing protocol, combining the benefits of ABC with a dual scenario of SVM and ANN.