# 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
Behavior threshold parameters were tuned against a set of 10,000 authenticated sessions with a false positive rate of 0.5% and false negative rate of 0.2%, on par with industry standards for high-security applications [70]. SecureDrone authentication protocol was verified with the AVISPA tool which is shown in Figure 4 by using all four back-ends--OFMC, CL-AtSe, SATMC, and TA4 SP--and each of them checking for important security properties. HLPSL-modeled, the protocol assigned roles to the User/Controller, Edge Server, Ground Control Station, and Drone. The verification outputs, indicated as \"SAFE\" in all tools, asserted the protocol's strength against numerous attack vectors. The major achievements were to provide mutual authentication, secrecy of the session key, message integrity, forward secrecy, and protection against replay. The protocol was highly resistant to man-in-the-middle, replay, and impersonation attacks using digital signatures, timestamps, nonces, sequence numbers, and two-factor authentication techniques. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Security Property** & **Verification Tool** & **Result** & **Description** \\ \hline Mutual Authentication & OFMC, CL-AtSe & SAFE & Protocol ensures all entities \\  & & & authenticate each other \\ Session Key Secrecy & OFMC, CL-AtSe, SATMC & SAFE & Session keys remain confidential \\  & & & between authenticated parties \\ Message Integrity & OFMC, SATMC & SAFE & Messages cannot be altered without \\  & & & detection \\ Forward Secrecy & CL-AtSe & SAFE & Compromise of current keys does not \\  & & & affect past communications \\ Replay Protection & OFMC, CL-AtSe & SAFE & Protocol resists replay of captured \\  & & & messages \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Security properties verification results. ## 4 Results

### Experimental Setup and Simulation Environment

The operation of the proposed SecureDrone authentication scheme was evaluated using thorough simulations in Network Simulator 2 (NS2) version 2.35, which simulated realistic Internet of Drones (IoD) deployment environments with varying numbers of drones, users, and network scenarios. The simulation parameters are shown in Table 2. The simulation was conducted on a 1000 m \(\times\) 1000 m space for 1000 s with varying drones from 5 to 50, varying users from 1 to 20, ground stations from two to eight, and edge servers from one to five. Drones followed a Random Waypoint mobility model with speeds between 0 and 20 m/s and a 100 m communication range for drone-to-GCS connections. The bandwidth allocation was 5 Mbps for D2G, 20 Mbps for G2E, and 50 Mbps for E2C communications. IEEE 802.11n was the MAC protocol employed, with a two-ray ground propagation model. AES-128-GCM for symmetric encryption and ECC-256 for asymmetric encryption ensured cryptographic security for the 512-byte data packets. Moreover, a hybrid machine learning model of LSTM and Random Forest was utilized for authentication, which was performed at each 300 s interval. Background traffic exhibited a Constant Bit Rate (CBR) with different intensities. For comparison with SecureDrone, four latest authentication protocols--LDAP, TAUROT, IoD-Auth, and LEMAP were utilized as baseline schemes. The performance was measured in terms of computational overhead (CPU time used for authentication tasks), authentication latency (request-to-authentication time), memory consumption, energy use, communication overhead (size and number of messages exchanged), scalability (performance when the network size is growing), and authentication success rate (successful authentication ratio under different attack modes). Figure 4: Security verification using AVISPA tool. ### Computational Overhead Analysis

Figure 5 shows how computational overhead (in milliseconds) scales with the number of drones for each protocol. SecureDrone always shows less computational demand in all scales of networks, with the performance difference growing as the number of drones grows. This shows better scalability in resource-scar settings.