# 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
Comparison with other protocols such as LDAP, TAUROT, IoD-Auth, and LEMAP demonstrates notable enhancement in relevant performance metrics of interest. SecureDrone holds a 30% reduction in computation overhead, 25% lower authentication delay, and 40% reduced memory usage without losing strict security guarantees. All of this validates that our protocol is particularly well designed for resource-constrained drone networks with real-time authentication at the cost of nothing else. Additionally, adaptive security is also achieved through the behavior analysis module that uses machine learning, making SecureDrone stronger against dynamic and adaptive types of attacks. SecureDrone uses ensemble learning methods such as Long Short-Term Memory (LSTM) networks and Random Forest classifiers to detect anomalous user activity and catch attacks prior to causing harm. This aspect greatly boosts impersonation attack resistance and unauthorized access attempts common in IoD environments. The security verification performed using the assistance of the AVISPA tool also reinforces the high resilience of SecureDrone to significant attacks including man-in-the-middle (MITM) attacks, replay attacks, and key compromise attempts. The timestamps employed along with nonces, digital signatures, and the application of elliptic curve cryptography (ECC) in SecureDrone secure cryptographic integrity firmly while the resource consumption is minimized to an absolute level. Scalability testing also highlights how practical SecureDrone is on large-scale drone networks. Even during heavy network loads, SecureDrone exhibits steady authentication performance, demonstrating the capability to carry an increasing quantity of drones and users without wasting resources in an observable manner. Scalability is achieved through optimization of session handling, distributed computation on edge servers, and cryptographically optimized functions. Although the envisioned authentication framework is specifically designed for the IoD environment, it can be adapted to other domains that have mobility restrictions and

Figure 12: Comparison of performance of different machine learning methods in behavioral authentication. scarce resources in the future, e.g., autonomous vehicular networks, underwater sensor networks, and wearable medical devices. These extensions can further show the flexibility and broader applicability of the framework. In spite of all these benefits, there are certain drawbacks. First, behavior analysis does improve security, but needs startup training data to create precise user profiles, and this may involve a deployment learning curve. Second, the protocol makes use of edge computing capabilities to run real-time behavior analysis, which may not be present in all IoD settings. Future work might look to explore fully decentralized authentication approaches that can counteract such limitations. ## 6 Conclusions

The SecureDrone authentication framework improves IoD security through a combination of machine-learning based behavior analysis with light-weight cryptographic schemes to provide strong security at minimal resource costs. The proposed SecureDrone provides a 30% decrease in processing, 25% decrease in authentication latency and 40% decrease in memory usage, when compared with the existing protocols of LDAP, TAU-ROT, IoD-Auth and LEMAP, making it extremely efficient for deployment in low-resource drone networks. The proposed SecureDrone uses LSTM and Random Forest algorithms to identify anomalous behaviors and provides adaptive defenses to manifestations of threats, such as impersonation and unauthorized access. Formal security verification via AVISPA demonstrates the proposed framework's resistance to man-in-the-middle, replay and key compromised attacks, as well as scalability testing demonstrating the proposed framework maintains performance predictability in high network load scenarios. The implementation of optimized cryptographic pipelines, session handling, and distributed processing across edge servers further improves efficiencies.