# 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  
The proposed security protocol includes three phases: registration, authentication, and verification. These phases all utilize machine learning methods for optimizing security using behavioral biometrics and telemetry analysis. The Algorithms 1-5 described below outline the execution of these phases for a solid framework for secure communication between users, drones, and the entity server. Explanation:  
User Registration--An ECC key pair is created at the beginning of the user registration process. The system stores the hashed user credentials and public key in a secure registry. The initial data on user interactions is used to derive behavioral features related to command usage and typing rhythm. A Random Forest (RF) classifier is trained using these features for anomaly detection, and an LSTM network is trained for sequential behavior modeling. Real and synthetic data are used to train both models, which are then stored in a behavior profile database. Finally, a registration token is generated by using the entity server's private key to sign the user's credentials and public key. ```
Input: UserID, Credentials, InitialBehaviorProfile

Output: Registration token, Public Key of the Entity Server (PK_ES)
```
1:(PK_U, SK_U)\(\leftarrow\) GenerateECCKeyPair()
2: Store UserID, PK_U, Hash(Credentials) in UserRegistry
3: behavioralFeatures \(\leftarrow\) ExtractBehavioralFeatures(InitialBehaviorProfile)
4:lstModel \(\leftarrow\) InitializeLSTMNetwork(inputSize = \(\mid\) behavioralFeatures \(\mid\), hiddenLayers = 2, hiddenSize = 64)
5:lstTrainingData \(\leftarrow\) GenerateSequentialBehaviorSamples(behavioralFeatures)
6:for epoch = 1 to 100 do
7: for each sequence in lstmTrainingData do
8: output \(\leftarrow\) lstmModel.Forward(sequence)
9: loss \(\leftarrow\) SequenceLoss(output, sequence)
10: lstmModel.Backpropagate(loss)
11: end for
12: end for
13: rfModel \(\leftarrow\) InitializeRandomForest(treeCount = 100, maxDepth = 10)
14: rfTrainingData \(\leftarrow\) GenerateBalancedTrainingSet(behavioralFeatures)
15: rfModel.Train(rfTrainingData)
16: Store (lstmModel, rfModel) in BehaviorDB under UserID
17: token \(\leftarrow\) SignECC(SK_ES, (UserID, PK_U, Timestamp))
18: return token, PK_ES ```
**Algorithm 1**User Registration
```
1:Input: DroneID, DroneType, Capabilities
2:Output: Drone Certificate
3: (PK_D, SK_D)\(\leftarrow\) GenerateECCKeyPair()
4: Store DroneID, PK_D, DroneType, Capabilities in DroneRegistry
5: telemetryFeatures \(\leftarrow\) ExtractTelemetryFeatures(DroneType, Capabilities)
6: droneLSTM \(\leftarrow\) InitializeLSTMNetwork(inputSize = 1 telemetryFeatures \(\mid\), hiddenLayers = 2, hiddenSize = 32)
7: PretrainDroneLSTM(droneLSTM, DroneType)
8: droneRF \(\leftarrow\) InitializeRandomForest(treeCount = 80, maxDepth = 8)
9: PretrainDroneRF(droneRF, DroneType)
10: Store (droneLSTM, droneRF) in DroneModelDB under DroneID
11: SecureStore(DroneID, SK_D, PK_ES)
12: cert \(\leftarrow\) SignECC(SK_ES, (DroneID, PK_D, Timestamp))
13: return cert ```
**Algorithm 2** Drone Registration

Explanation:

Drone Registration--During registration of the drone, the system creates an ECC key pair and registers metadata of the drone like type and capabilities. Telemetry features like flight patterns, sensor readings, control latencies are extracted. A pre-trained LSTM model for drones is initiated and pre-trained with manufacturer-provided datasets mimicking normal operation of the drones. Simultaneously, a Random Forest classifier is trained to detect anomalies in real-time. The models are stored, and the drone is equipped with its private key and entity server public key. A digitally signed certificate by the server completes the secure enrollment.