## 4 Results and Discussion  
The proposed work has been implemented in the Ns-3 network simulator, which is depicted in Figure 4. It utilizes the novel BSHNN algorithm with searchable encryption and blockchain technology. Initially, create a mobile ad hoc network structure in the NS-3 simulator, randomly assign malicious nodes to send or receive data, and authenticate incoming users using an append-only blockchain. Log the network traffic and other parameters in a CSV file, import it in Python, and classify vulnerabilities using the binarized deep spiking capsule fire hawk neural network. The details of the simulation parameters are presented in Table 2.  
Figure 4: Network simulation environment. Figure 4 provides an intuitive view with respect to the approved nodes. A secure hashed number (hexadecimal) is assigned with each approved node as shown in the figure. Every blockchain is also referred to by a random or semi-random number (nonce) that is generated for a specific use, typically in cryptographic communication. The nonce is crucial because it ensures the randomness and uniqueness of each attempt at solving the cryptographic puzzle, making it computationally difficult to manipulate the blockchain. The above parameters comprehensively define the simulation environment by encompassing spatial dimensions, time duration, network components (Mobile User (MU), APs, switches, and controllers), mobility characteristics, communication protocols, and intricate details of the blockchain simulation (such as block size, header structure, proof type, and hash generation). The incorporation of these details in Table 3 serves to improve transparency and reproducibility in the simulation, facilitating a comprehensive grasp of the conditions under which the evaluation of the Ns-3 with the BSHNN algorithm takes place. The network simulation environment of the proposed method is depicted in Figure 4. \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Simulation area & 500 m \(\times\) 500 m \\ \hline Simulation time & 45 s \\ \hline Number of APs & 8 \\ \hline Number of MUs & 50 \\ \hline Number of controllers & 1 \\ \hline Number of switches & 7 \\ \hline Attacks & 20\% \\ \hline Flow type & MAC-IP flow \\ \hline SDN controller & OpenFlow \\ \hline Protocol used & TCP \\ \hline Packet length & 1500 \\ \hline Mobility of MUs & 0–2 m/s \\ \hline Mobility model of MU & RandomWalk2D \\ \hline Interval time & Random \\ \hline Block size & 100 Transactions \\ \hline Block header & Previous block hash + timestamp + nonce + difficulty target \\ \hline Number of transactions & 600 in 45 s \\ \hline Hash generation & SHA256 \\ \hline \end{tabular}
\end{table} 
Table 2: Simulation Parameters. \begin{table}
\begin{tabular}{c c} \hline
**Metrics** & **Proposed Approach** \\ \hline Detection rate (\%) & 96.41 \\ \hline Authentication time (s) & 16.2 \\ \hline Delay (s) & 0.09 \\ \hline Throughput (Mbps) & 428 \\ \hline Packet loss ratio (\%) & 1.5 \\ \hline Accuracy (\%) & 98.02 \\ \hline Precision (\%) & 96.40 \\ \hline Recall (\%) & 97.53 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance metrics for the proposed method. The time required for detection and authenticating secure networks becomes a crucial aspect to determine the consistency of any proposal with blockchain networks. Keeping this in mind, the proposed algorithm demonstrates remarkable accuracy in classifying network events by achieving a detection rate of 96.41%. Particularly, the authentication time is impressively low at 16.2 s, underscoring the algorithm's efficiency in authenticating mobile devices. With a minimal delay of 0.09 s, encompassing propagation, access, switching, queuing, and controller delays, the proposed approach ensures swift and effective packet transmission. Thus, the proposal clearly assures its candidature to be used in a practical scenario. The proposal accounts for a high throughput (i.e., 428 Mbps) and emphasizes its capability for successful data transmission.