## 5 Conclusions and Future Scope  
This paper aims to resolve the security issue with mobile ad-hoc networks. As a result, several methods are initially investigated in order to identify the network and attributes of the attacker. This study analyzes the features of deep-learning-based security detection methods, which may be useful in MANETs for developing situational suitable strategies. Firstly, the input videos are collected, and then the proposed study detected black-hole nodes that behave in a unique way from other nodes in specific conditions. This simple detection method can effectively recognize such characteristics. Next, trust value is used to assess data and network trust levels to identify attacks and offer security solutions. Then, the OLSR protocol provides an effective routing system that is employed in mobile ad-hoc networks, to decrease network-related traffic data. Then, the osprey optimization method fine-tuned the parameters such as node-stability degree and link-stability degree. Thus, interplanetary file system technology enables blockchain storage to increase the security of the MANET's data for sharing files and applications. Finally, the validation process utilizing the delegated proof-of-stake method is possible because of the system's higher consensus, which results in an enhanced delegation limit. The proposed model performs better in terms of AED (0.172), DP (57.6), PDR (99.8), end-to-end delay (19.1), ROH (41.7), and throughput (9100). In the future, the system will be extended to detect a wider variety of network attacks by adding more criteria to the specified attack-detection method. The proposed method will be expanded even further to carry out efficient data-packet transfer for extensive network setups with the goal of achieving optimal link quality between mobile nodes in MANETs. Increased capacity and network durability are directly correlated with improved node links. Thus, the hyperparameters of the proposed method will be optimized, utilizing several optimization techniques, including genetic algorithms. Furthermore, other types of wireless networks, such as vehicular ad-hoc networks (VANETs) will be assessed to utilize the proposed approach in the future. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **20** & **40** & **60** & **80** & **100** \\ \cline{2-6}  & **Proposed** & 0.06 & 0.075 & 0.134 & 0.168 & 0.174 \\ \cline{2-6}  & SRABC & 0.11 & 0.117 & 0.16 & 0.19 & 0.2 \\ \cline{2-6}  & AODV & 0.21 & 0.18 & 0.189 & 0.22 & 0.23 \\ \cline{2-6}  & DSR & 0.199 & 0.21 & 0.22 & 0.22 & 0.24 \\ \cline{2-6}  & OLSR & 0.09 & 0.103 & 0.154 & 0.2 & 0.21 \\ \hline \multirow{4}{*}{Packetdelivery ratio} & **Proposed** & **94.3** & **96.2** & **97.4** & **98.9** & **99.5** \\ \cline{2-6}  & SRABC & 92.7 & 95 & 95 & 97.5 & 98.7 \\ \cline{2-6}  & AODV & 86 & 87 & 92 & 93.5 & 96 \\ \cline{2-6}  & DSR & 72.5 & 76 & 80 & 88.2 & 92 \\ \cline{2-6}  & OLSR & 84 & 88 & 93 & 94 & 97 \\ \hline \multirow{4}{*}{Throughput} & **Proposed** & **932** & **1232** & **1642** & **2212** & **2902** \\ \cline{2-6}  & SRABC & 682 & 982 & 1302 & 1902 & 2602 \\ \cline{2-6}  & HEDAR & 502 & 852 & 1152 & 1702 & 1982 \\ \cline{2-6}  & SETORD & 402 & 702 & 952 & 1552 & 1752 \\ \cline{2-6}  & OLSR & 902 & 1002 & 1352 & 1502 & 1802 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Values of additional videos for training and testing by varying time and nodes. **Author Contributions:** Conceptualization, H.A.A. and H.A.A.A.-A. ; methodology, H.A.A. ; software, H.A.A. ; validation, H.A.A.A.-A. ; formal analysis, H.A.A.A.-A. ; investigation, H.A.A.A.-A. ; resources, H.A.A.A.-A. ; data curation, H.A.A. and H.A.A.A.-A. ; writing--original draft preparation, H.A.A. and H.A.A.A.-A. ; writing--review and editing, H.A.A.A.-A. ; visualization, H.A.A.-A. ; supervision, H.A.A.A.-A. ; project administration, H.A.A.A.-A. All authors have read and agreed to the published version of the manuscript. This research received no external funding.