# 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
This scalability is made possible by effective parallel processing of authentication requests, optimized session management with low-state information, distributed processing across edge servers, effective handling of concurrent authentication sessions, and low computational complexity of cryptographic operations. ### Authentication Success Rate Under Attack Scenarios

To compare security effectiveness, we quantified the authentication success rate under different attack conditions, and Figure 11 shows the comparison. This graph illustrates the strength of each protocol against more intense attack scenarios. SecureDrone has over 85% success rate even in extreme DoS conditions (80%), illustrating its strong security architecture and attack resistance compared to other methods. The results show that SecureDrone has higher authentication success rates under all attack conditions, especially under heavy DoS attacks, where it has an 85.7% success rate under 80% DoS attack condi

Figure 10: 3D visualization showing authentication time as both drone count and user count increases. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protocol** & \begin{tabular}{c} **Message** \\ **Count** \\ \end{tabular} & \begin{tabular}{c} **Average Size** \\ **(bytes)** \\ \end{tabular} & \
\begin{tabular}{c} **Total Size (KB)** \\ \end{tabular} &
\begin{tabular}{c} **Authentication** \\ **Rounds** \\ \end{tabular} \\ \hline SecureDrone & 4 & 328 & 1.28 & 2 \\ LDAP & 6 & 319 & 1.87 & 3 \\ TAUROT & 5 & 338 & 1.65 & 2.5 \\ IoD-Auth & 7 & 328 & 2.24 & 3.5 \\ LEMAP & 5 & 315 & 1.54 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Communication Overhead Detailed Comparison. tions, compared to 62.3% for LDAP, 68.7% for TAUROT, 65.2% for IoD-Auth, and 67.4% for LEMAP. This improved security performance is credited to dual-factor authentication based on cryptography credentials and behavioral analysis, high-quality anti-replay capabilities using timestamps and nonces, aggressive mutual authentication against MITM attacks, effective DoS protection using rate limiting and adaptive challenge complexity, and behavioral-based anomaly detection for impersonation attempts. ### Performance Comparison Summary

Table 6 presents a detailed comparison of all protocols on major performance metrics, aggregating their efficiency in computational overhead, authentication latency, memory consumption, energy expenditure, communication overhead, scalability, and security effectiveness. The comparison identifies SecureDrone's strengths in maximizing resource utilization, minimizing authentication time, reducing communication expenses, and maximizing security resilience against different attack scenarios. ### Impact of Deep Learning Model Complexity

Figure 12 illustrates performance comparisons of different machine learning methods for behavioral authentication. Although Deep LSTM has the highestperformance(\(\mathtt{\sim}\)99%), the RF + LSTM hybrid model implemented (\(\mathtt{\sim}\)98%) offers almost the same security with

\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Metric** & **SecureDrone** & **LDAP** & **TAUROT** & **IoD-Auth** & **LEMAP** & **Improvement** \\ \hline Computational & & & & & & & 30\% avg. \\ Overhead (ms) & 31.8 & 48.7 & 47.8 & 55.2 & 43.8 &
\begin{tabular}{c} 30\% avg. \\ reduction \\ 25\% avg. \\ reduction \\ 40\% avg. \\ reduction \\ 35\% avg. \\ reduction \\ 35\% avg. \\ reduction \\



much lower computational demands, which is an ideal balance between security and resource utilization for drone environments. ## 5 Discussion

The SecureDrone authentication protocol settles the key security issues surrounding the Internet of Drones (IoD) effectively. The protocol effectively balances between low-resource-utility and high-security attributes by integrating lightweight cryptographic algorithms with behavior analysis using machine learning. The main contribution of SecureDrone is to enable more accurate authentication with minimal computational overhead.