# An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
## 3 Materials and Methods
### System Model and Problem Formulation
#### 3.1.4 Proposed Authentication Protocol  
The SecureDrone authentication protocol is a robust security mechanism that is suitable for the Internet of Drones (IoD). It uses machine learning-based behavior analysis of the users and lightweight cryptographic primitives for providing secure and lightweight authentication with low latency and processing overhead. There exists a cloud server, edge server, Ground Control Station, user controllers, and drones in the protocol structure; each includes domain-specific behavior analysis, authentication, and cryptographic modules. An architecture is shown in Figure 3.  
There are three stages in the authentication procedure: the registration stage, in which the users, drones, and control stations are registered; the authentication stage, based on behavior analysis and cryptographic primitives for trusted authentication; and the session management stage, with secure communication through constant re-authentication. A combination model incorporating a Random Forest classifier for classifying categorical attributes and a Long Short-Term Memory (LSTM) network for identifying time patterns identifies user behavior patterns such as command use, flight path requests, and control input attributes to specify a behavioral profile. The Random Forest and LSTM combination was chosen particularly by ablation studies that proved its performance to be better than for single-model approaches. This blended model supports current research by [PERSON] et al. (2017), which illustrated the performance of ensemble methods to surpass single classifiers for drone behavior authentication by 12-18%. Our choice of parameters for the Random Forest (100 trees, maximum depth of 10) and LSTM network (128 hidden units, 2-layer architecture) was tuned using grid search experiments to trade off accuracy against computational cost. User behavior analysis through machine learning is a new component of this protocol. By computing the deviation between the profile and the current behavior vector, the protocol computes a Behavior Deviation Score (BDS), which, in case of deviations greater than a threshold value, triggers additional authentication challenges. SecureDrone  
Figure 3: SecureDrone authentication protocol architecture.  
employs SHA-256 for hashing, AES-GCM for session encryption, Elliptic Curve Cryptography (ECC) for key exchange and digital signatures, and nonce-timestamp algorithms to prevent replay attacks to ensure security with minimal computational overhead. To ensure that only authorized users and drones communicate, the authentication process includes one-time user registration, behavior analysis when receiving an authentication request, challenge-response verification, and session key distribution upon successful authentication. SecureDrone ensures quick and scalable authentication in IoD environments by offloading model training onto cloud servers while preserving real-time inference on edge servers. This enhances security without compromising operational efficiency.