# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 3 Materials and Methods
### System Model and Problem Formulation
#### 3.1.6 Protocol Phases
However, planning for the initial training data requirements and a dependence on edge computing for processing to provide real time responses may not be suitable for all deployments. This framework has immediate utility in the military drone domain, commercial delivery networks and smart city surveillance systems where a blend of resource efficiency and strong security is paramount. In military scenarios, SecureDrone's ability to withstand sophisticated attacks allows for secure tactical communications in contested environments. In commercial drone delivery operations, the low computational overhead of our framework means that it can be implemented on low-cost hardware while maintaining sufficient security to operate in populated areas. For public safety agencies, the behavioral analysis portion of the framework offers additional security by identifying drones subjected to malicious attacks, and providing indicators of unauthorized control attempts during critical incidents. SecureDrone's demonstrated improvements in performance, from this work, allows for rapid rollout and adoption in production environments, while also establishing a new standard in IoD security protocols. Current and future work will continue to improve decentralized authentication, consider blockchain to add security to IoD operations, and enhance the behavior analysis model to add more layers of security to IoD authentication frameworks, and consider extending IoD authentication frameworks operations to new areas including agricultural monitoring, infrastructure inspection and fully autonomous drone swarms. Conceptualization, V.K.P., S.P. and A.R. ; investigation, V.K.P., S.P. and S.K.J. ; methodology, V.K.P. and X.L. ; supervision, S.P. and R.S.R. ; validation, A.R., X.L. and R.S.R. ; writing, V.K.P., S.P. and S.K.J. ; review and editing, A.R., S.P., X.L. and R.S.R. All authors have read and agreed to the published version of the manuscript. This research received no external funding. ## Conflicts of Interest: The authors declare no conflict of interest. ## References

* (1) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Survey on UAV cellular communications: Practical aspects, standardization advancements, regulation, and security challenges. _IEEE Commun. Surv. Tutor._**2019**, _21_, 3417-3442. [CrossRef]
* (2) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Adversarial Label-Flipping Attack and Defense for Anomaly Detection in Spatial Crowdsourcing UAV Services. _IEEE Trans. Consum. Electron._**2024**, 1. [CrossRef]
* (3) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Scientometric analysis in the field of big data and artificial intelligence in industry. In Proceedings of the 2022 1 st International Conference on Information System & Information Technology (ICISIT), Virtual Conference, 27-28 July 2022; IEEE. Piscataway, NJ, USA, 2022. * (4) [PERSON]; [PERSON]; [PERSON]; [PERSON] AI meets UAVs: A survey on AI empowered UAV perception systems for precision agriculture. _Neurocomputing_**2023**, _518_, 242-270. [CrossRef]
* (5) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Performance analysis of UAV enabled disaster recovery networks: A stochastic geometric framework based on cluster processes. _IEEE Access_**2018**, \(6\), 26215-26230. [CrossRef]
* (6) [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] Quality-of-service-centric design and analysis of unmanned aerial vehicles. _Sensors_**2022**, _22_, 5477. [CrossRef]
* (7) [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] UAV-enabled intelligent transportation systems for the smart city: Applications and challenges. _IEEE Commun. Mag._**2017**, _55_, 22-28. * (8) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON] Real-Time Threat Detection and Response Using Computer Vision in Border Security.