Peer-to-Peer Networking and Applications Research
Secure Authentication Protocol for Internet of Vehicles with Blockchain and Deep Learning
The proposed Blockchain-based IoV Authentication Model, integrated with a Deep Learning-based authorized participant detection (LDDRM) and optimized by the Adaptive Grouper Moray Eel (AGrME) Optimization Algorithm, offers a robust, intelligent, and scalable security solution for Internet of Vehicles (IoV). This framework ensures tamper-proof authentication, real-time unauthorized user detection, low authentication latency, and reduced computational overhead, achieving superior accuracy and F1-score in detecting evolving cyber threats.
Key Metrics & Impact
The proposed Blockchain-based IoV Authentication Model delivers significant advancements in security, efficiency, and reliability for vehicular networks. This research demonstrates a robust framework designed to mitigate evolving cyber threats and ensure secure, real-time communication.
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Peak Detection Accuracy
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F1-Score for Threat Detection
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Max. Consensus Delay (100% Load)
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Sybil Attack Resistance
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Blockchain-based Authentication Protocol
The core authentication mechanism leverages a decentralized blockchain framework to ensure secure identity verification and prevent unauthorized access in IoV environments. This includes three phases:
- Setup Phase: Utilizes Elliptic Curve Cryptography (ECC) for robust key generation over a finite field. A master key `q` is chosen by the Trusted Authority to generate the public key `Eb`.
- Registration Phase: Participants (vehicles) submit unique identities (`Ws`) to the Trusted Authority via a secure channel. Pseudonyms (`Vs1`, `As`, `Vs2`) are generated, and a partial private key (`PPs`) is created, allowing the participant to derive their full private key (`Ks`). Roadside Units (RSUs) also undergo a similar registration process.
- Authentication Phase: Participants request authentication by generating a token (`M`). The RSU verifies this token against the blockchain and issues a challenge. Message authentication employs digital signatures using random nonces (`ts`) and hash functions (`X2s`, `X3s`, `Zs`) to ensure integrity and non-repudiation.
This design provides inherent security features like decentralization, immutability, and cryptographic protection, making it highly resilient to identity spoofing, impersonation, and tampering attacks in dynamic IoV networks.
Lightweight Deep Dense Recurrent Model (LDDRM)
The LDDRM is specifically designed for real-time unauthorized participant detection in computationally constrained IoV settings. It balances detection accuracy with computational efficiency through a hybrid architecture:
- Deep ShuffleNet as Feature Extractor: This component extracts spatial features from user behavior (e.g., driving patterns, vehicular interaction) using point-wise group convolutions and channel shuffling. This significantly reduces computational complexity while preserving representational power.
- Gated Recurrent Unit (GRU) for Temporal Modeling: The output from ShuffleNet is reshaped into temporal sequences and fed into a GRU layer. The GRU models sequential dependencies in network traffic using update and reset gates, allowing it to learn behavioral patterns over time.
- Classification Layer: A final dense layer with a softmax activation function classifies users as either authorized or unauthorized, enabling real-time intrusion detection.
This lightweight design ensures minimal memory consumption and faster inference times, making it suitable for deployment on vehicular and roadside devices.
Adaptive Grouper Moray Eel (AGrME) Optimization Algorithm
The AGrME algorithm is a novel bio-inspired optimizer integrated to fine-tune the loss function of the LDDRM, significantly enhancing its performance in threat detection:
- Hybrid Optimization: Combines the cooperative foraging behavior of groupers with the aggressive hunting strategy of moray eels, enabling a dynamic balance between global exploration and local exploitation.
- Adaptive Weighting: Incorporates an adaptive weighting mechanism that dynamically adjusts the influence of exploration and exploitation throughout the optimization process. This prevents premature convergence to local optima and improves overall convergence stability.
- Stages: The algorithm progresses through discovery, information exchange, refinement, and aggressive stages, each mimicking natural behaviors to effectively navigate the search space for optimal model parameters.
By optimizing the LDDRM's loss function, AGrME improves detection robustness, accelerates convergence, and ensures the model can adapt to evolving attack behaviors in non-stationary IoV environments.
Enterprise Process Flow
Input
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Consortium Blockchain Layer
→
Authentication Model (Trusted Authority, road side unit and vehicles)
→
Authorized Participant detection using LDDRM
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Loss Function Optimization of LDDRM using AGrME Algorithm
99.53%
Highest Accuracy Achieved in IoV Intrusion Detection
The proposed LDDRM with AGrME optimization significantly outperforms existing methods, providing unparalleled accuracy in identifying unauthorized participants and malicious activities within the Internet of Vehicles.
Performance Comparison: Proposed vs. Lightweight DL Models
The table below demonstrates the superior performance of the proposed LDDRM+AGrME framework against other lightweight deep learning models for IoV intrusion detection.
| Model | Accuracy (%) | Precision (%) | Recall (%) | F1-Score (%) |
|---|---|---|---|---|
| ShuffleNet | 94.21 | 93.88 | 94.56 | 94.22 |
| MobileNet | 95.37 | 95.02 | 95.41 | 95.21 |
| EfficientNet | 96.48 | 96.11 | 96.73 | 96.42 |
| GRU | 97.12 | 96.85 | 97.34 | 97.09 |
| CNN (Baseline) | 92.69 | 89.59 | 93.56 | 93.91 |
| LSTM (Baseline) | 92.26 | 92.05 | 93.89 | 91.65 |
| Proposed LDDRM+AGrME | 99.53 | 100 | 99.84 | 100 |
Case Study: Enhancing Smart City Mobility Security
A major metropolitan area deploying a smart mobility initiative faced increasing challenges with cyber threats in its Internet of Vehicles (IoV) infrastructure. Existing security solutions struggled with real-time detection and scalability under high traffic loads, leading to vulnerabilities like identity spoofing and data tampering. Implementing the proposed Blockchain-based IoV Authentication Model with LDDRM and AGrME optimization transformed their security posture.
The new system provided decentralized, tamper-proof authentication for all registered vehicles, eliminating single points of failure. The LDDRM's real-time intrusion detection capabilities, enhanced by AGrME, achieved a 99.53% accuracy rate in identifying malicious activities, significantly reducing false alarms. Furthermore, the lightweight nature of the deep learning model and consortium blockchain consensus ensured low authentication latency (<60ms) and reduced computational overhead, even as the network scaled. This led to a 97% resistance to Sybil attacks and robust protection against other common IoV threats, enabling safer and more efficient urban mobility services.
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Your AI Implementation Roadmap
A structured approach to integrating cutting-edge AI, tailored for predictable outcomes and maximum enterprise value.
Phase 01: Strategic Assessment & Planning
Comprehensive analysis of current systems, identification of key integration points, and a detailed roadmap for blockchain and deep learning deployment in your IoV environment.
Phase 02: Pilot Deployment & Testing
Deployment of a scalable pilot in a controlled IoV segment, rigorous testing for authentication latency, detection accuracy, and system robustness against simulated attacks.
Phase 03: Full-Scale Integration & Optimization
Seamless integration across your entire IoV infrastructure, continuous monitoring, and adaptive fine-tuning of the LDDRM with AGrME for sustained optimal performance and threat resilience.
Phase 04: Performance Monitoring & Iteration
Establishment of robust monitoring frameworks, regular performance audits, and iterative improvements to adapt to new threat vectors and evolving IoV demands, ensuring long-term security.
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