An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing
Abstract
:1. Introduction
- The design of a lightweight authentication framework based on a hybrid machine learning model that incorporates random forest and LSTM models with comparable security and significantly lower computational demands.
- Complete security verification by way of formal verification using AVISPA (Automated Validation of Internet Security Protocols and Applications) and informal security analysis to show immunity to typical attack mechanisms such as replay attacks, man-in-the-middle attacks, and impersonation attacks.
- Extensive performance assessment with NS2 simulation, achieving a 43% decrease in computational overhead, 37% reduction in communication latency, and 28% less energy expenditure than current IoD authentication mechanisms.
2. Related Work
2.1. IoD Security Frameworks
2.2. Lightweight Authentication Protocols
2.3. Machine Learning for IoD Security
2.4. Security Evaluation Methodologies
2.5. Research Gap
3. Materials and Methods
3.1. System Model and Problem Formulation
3.1.1. IoD Network Architecture
3.1.2. Security Requirements
3.1.3. Threat Model and Problem Formulation
3.1.4. Proposed Authentication Protocol
3.1.5. Training Process of Machine Learning Models
3.1.6. Protocol Phases
Algorithm 1: User Registration |
Input: UserID, Credentials, InitialBehaviorProfile Output: Registration token, Public Key of the Entity Server (PK_ES) 1: (PK_U, SK_U)← GenerateECCKeyPair() 2: Store UserID, PK_U, Hash(Credentials) in UserRegistry 3: behavioralFeatures ← ExtractBehavioralFeatures(InitialBehaviorProfile) 4: lstmModel ← InitializeLSTMNetwork(inputSize = |behavioralFeatures|, hiddenLayers = 2, hiddenSize = 64) 5: lstmTrainingData ← GenerateSequentialBehaviorSamples(behavioralFeatures) 6: for epoch = 1 to 100 do 7: for each sequence in lstmTrainingData do 8: output ← lstmModel.Forward(sequence) 9: loss ← SequenceLoss(output, sequence) 10: lstmModel.Backpropagate(loss) 11: end for 12: end for 13: rfModel ← InitializeRandomForest(treeCount = 100, maxDepth = 10) 14: rfTrainingData ← GenerateBalancedTrainingSet(behavioralFeatures) 15: rfModel.Train(rfTrainingData) 16: Store (lstmModel, rfModel) in BehaviorDB under UserID 17: token ← SignECC(SK_ES, {UserID, PK_U, Timestamp}) 18: return token, PK_ES |
Algorithm 2: Drone Registration |
Input: DroneID, DroneType, Capabilities Output: Drone Certificate 1: (PK_D, SK_D)← GenerateECCKeyPair() 2: Store DroneID, PK_D, DroneType, Capabilities in DroneRegistry 3: telemetryFeatures ← ExtractTelemetryFeatures(DroneType, Capabilities) 4: droneLSTM ← InitializeLSTMNetwork(inputSize = |telemetryFeatures|, hiddenLayers = 2, hiddenSize = 32) 5: PretrainDroneLSTM(droneLSTM, DroneType) 6: droneRF ← InitializeRandomForest(treeCount = 80, maxDepth = 8) 7: PretrainDroneRF(droneRF, DroneType) 8: Store (droneLSTM, droneRF) in DroneModelDB under DroneID 9: SecureStore(DroneID, SK_D, PK_ES) 10: cert ← SignECC(SK_ES, {DroneID, PK_D, Timestamp}) 11: return cert |
Algorithm 3: User Authentication |
Input:
UserID, AuthRequest = {reqID, timestamp, nonce, signature} Output: Encrypted challenge token or authentication failure 1: if CurrentTime()-timestamp > TimeWindow then 2: return AuthFailure(“Request expired”) 3: end if 4: userPK ← UserRegistry.getPK(UserID) 5: if not VerifyECC(userPK, signature, {reqID, timestamp, nonce}) then 6: return AuthFailure(“Invalid signature”) 7: end if 8: profile ← BehaviorDB.getProfile(UserID) 9: lstmModel ← profile.LSTM 10: rfModel ← profile.RF 11: challenge ← GenerateUserBehaviorChallenge(profile) 12: challengeToken ← EncryptECC(userPK, {challenge, nonce + 1}) 13: return challengeToken |
Algorithm 4: Verify User Response |
UserID, Response = {challenge, responseData, signature} is the input. Output: AuthFailure or AuthSuccess with session key 1: UserPK ← UserRegistry.getPK (UserID) 2: If VerifyECC(userPK, signature, {challenge, responseData}) is not present, then 3: return AuthFailure(“Invalid response”) 4: terminate if 5: Extract Behavior Features (responseData)← currentBehavior 6: behaviorSeq ← FormatBehaviorAsSequence(currentBehavior) 7: predictedSeq ← lstmModel.Forward(behaviorSeq) 8: SequenceLoss(predictedSeq, behaviorSeq[1:])← loss 9: lstmScore ← exp(−loss) 10: FormatBehaviorAsFeatureVector(currentBehavior)← rfVector 11: classProbs ← rfModel.PredictProbabilities(rfVector) 12: rfAnomaly ← 1 − classProbs[1] 13: behaviorScore ← 0.6 × lstmScore + 0.4 × (1 − rfAnomaly) 14: if behaviorScore is less than behavior threshold, then 15: Log(UserID, “Behavior anomaly”, behaviorScore) 16: AuthFailure(“Behavior anomaly detected”) is returned. 17: terminate if 18: lstmModel.Update(learningRate = 0.01, behaviorSeq) 19: rfModel.Update(rfVector, isPositive = TRUE) 20: BehaviorDB.update(UserID, currentBehavior, lstmModel, rfModel) 21: GenerateRandomKey(128) ← sessionKey 22: return AuthSuccess, encryptedKey 23: encryptedKey ← EncryptECC(userPK, sessionKey) |
Algorithm 5: Verify Drone Response |
Input: DroneID, Response = {challenge, telemetryData, signature} Output: AuthSuccess with encrypted session key or failure 1: dronePK ← DroneRegistry.getPK(DroneID) 2: if not VerifyECC(dronePK, signature, {challenge, telemetryData}) then 3: return AuthFailure(“Invalid drone response”) 4: end if 5: telemetryFeatures ← ExtractTelemetryFeatures(telemetryData) 6: seq ← FormatTelemetryAsSequence(telemetryFeatures) 7: predicted ← droneLSTM.Forward(seq) 8: seqScore ←CalculateSequenceSimilarity(predicted, seq) 9: vector ← FormatTelemetryAsFeatureVector(telemetryFeatures) 10: prob ← droneRF.PredictProbabilities(vector) 11: anomaly ← 1 − prob[1] 12: authScore ← 0.5 × seqScore + 0.5 × (1 − anomaly) 13: if authScore < DroneAuthThreshold then 14: Log(DroneID, “Compromised drone”, authScore) 15: return AuthFailure(“Telemetry mismatch”) 16: end if 17: droneLSTM.Update(seq) 18: droneRF.Update(vector, isNormal = TRUE) 19: DroneModelDB.update(DroneID, droneLSTM, droneRF) 20: sessionKey ← GenerateRandomKey(128) 21: encryptedKey ← EncryptECC(dronePK, sessionKey) 22: return AuthSuccess, encryptedKey |
3.1.7. Security Analysis and Verification
4. Results
4.1. Experimental Setup and Simulation Environment
4.2. Computational Overhead Analysis
4.3. Authentication Latency
4.4. Memory Usage Analysis
4.5. Energy Consumption
4.6. Communication Overhead
4.7. Scalability Analysis
4.8. Authentication Success Rate Under Attack Scenarios
4.9. Performance Comparison Summary
4.10. Impact of Deep Learning Model Complexity
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Security Property | Verification Tool | Result | Description |
---|---|---|---|
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 |
Parameter | Value |
---|---|
Simulator | NS2 (version 2.35) |
Simulation duration | 1000 s |
Simulation area | 1000 m × 1000 m |
Number of drones | 5–50 |
Number of users | 1–20 |
Number of ground stations | 2–8 |
Number of edge servers | 1–5 |
Drone mobility model | Random Waypoint |
Drone speed | 0–20 m/s |
Communication range | 100 m(drone-to-GCS) |
Bandwidth | 5 Mbps (D2G), 20 Mbps (G2E), 50 Mbps (E2C) |
MAC protocol | IEEE 802.11n |
Propagation model | Two-ray ground |
Packet size | 512 bytes |
Cryptographic algorithm | ECC-256 (asymmetric), AES-128-GCM (symmetric) |
Machine learning model | Ensemble (LSTM + Random Forest) |
Authentication frequency | Every 300 s |
Background traffic | CBR (varying intensity) |
Operation | SecureDrone | LDAP | TAUROT | IoD-Auth | LEMAP |
---|---|---|---|---|---|
Digital Signature Generation | 4.2 | 7.8 | 6.5 | 8.9 | 5.3 |
Signature Verification | 5.1 | 6.9 | 6.2 | 8.4 | 5.8 |
Key Exchange | 3.8 | 5.7 | 5.3 | 7.2 | 4.5 |
Symmetric Encryption | 0.7 | 0.8 | 0.9 | 0.8 | 0.8 |
Symmetric Decryption | 0.8 | 0.9 | 1.0 | 0.9 | 0.9 |
Hashing | 0.6 | 0.7 | 0.7 | 0.8 | 0.7 |
Behavior Analysis | 5.2 | N/A | 5.8 | N/A | 4.7 |
Total | 20.4 | 22.8 | 26.4 | 27.0 | 22.7 |
Component | SecureDrone | LDAP | TAUROT | IoD-Auth | LEMAP |
---|---|---|---|---|---|
Cryptographic Keys | 28 | 52 | 48 | 64 | 42 |
Session States | 32 | 48 | 42 | 55 | 38 |
Behavior Profiles | 45 | N/A | 51 | N/A | 47 |
Authentication Cache | 18 | 42 | 35 | 47 | 31 |
Code Footprint | 50 | 65 | 60 | 72 | 58 |
Total | 173 | 207 | 236 | 238 | 216 |
Protocol | Message Count | Average Size (bytes) | Total Size (KB) | Authentication Rounds |
---|---|---|---|---|
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 |
Metric | SecureDrone | LDAP | TAUROT | IoD-Auth | LEMAP | Improvement |
---|---|---|---|---|---|---|
Computational Overhead (ms) | 31.8 | 48.7 | 47.8 | 55.2 | 43.8 | 30% avg. reduction |
Authentication Latency (ms) | 102.3 | 156.7 | 150.4 | 168.5 | 135.2 | 25% avg. reduction |
Memory Usage (KB) | 258 | 402 | 384 | 453 | 368 | 40% avg. reduction |
Energy Consumption (mJ) | 42.5 | 68.3 | 63.7 | 75.4 | 57.9 | 35% avg. reduction |
Communication Messages | 4 | 6 | 5 | 7 | 5 | 28% avg. reduction |
Message Size (KB) | 1.28 | 1.87 | 1.65 | 2.24 | 1.54 | 29% avg. reduction |
Auth. Success (No Attack) (%) | 99.8 | 99.7 | 99.7 | 99.6 | 99.7 | 0.1% improvement |
Auth. Success (Under Attack) (%) | 95.6 | 85.5 | 88.4 | 87.2 | 87.7 | 9% avg. improvement |
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Pandey, V.K.; Prakash, S.; Ranjan, A.; Jha, S.K.; Liu, X.; Rathore, R.S. An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing. Designs 2025, 9, 61. https://doi.org/10.3390/designs9030061
Pandey VK, Prakash S, Ranjan A, Jha SK, Liu X, Rathore RS. An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing. Designs. 2025; 9(3):61. https://doi.org/10.3390/designs9030061
Chicago/Turabian StylePandey, Vivek Kumar, Shiv Prakash, Aditya Ranjan, Sudhanshu Kumar Jha, Xin Liu, and Rajkumar Singh Rathore. 2025. "An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing" Designs 9, no. 3: 61. https://doi.org/10.3390/designs9030061
APA StylePandey, V. K., Prakash, S., Ranjan, A., Jha, S. K., Liu, X., & Rathore, R. S. (2025). An Efficient Framework for Secure Communication in Internet of Drone Networks Using Deep Computing. Designs, 9(3), 61. https://doi.org/10.3390/designs9030061