Bio-Inspired AI Frameworks for Privacy-Aware Network Virtualization in Autonomous Driving Systems
DOI:
https://doi.org/10.15680/IJCTECE.2025.0803006Keywords:
Autonomous Driving, Network Function Virtualization, Bio-Inspired AI, Privacy, Blockchain, Federated Learning, CybersecurityAbstract
Autonomous driving systems demand robust, secure, and adaptive communication infrastructures to ensure safety, interoperability, and efficiency in connected vehicular environments. Network Function Virtualization (NFV) has emerged as a promising paradigm for flexible service orchestration in vehicular networks. However, NFV introduces challenges related to privacy, security, interoperability, and real-time adaptability. This paper proposes a Bio-Inspired AI Framework that integrates Artificial Immune Systems (AIS), Genetic Algorithms (GA), and Swarm Intelligence with advanced privacy-preserving techniques such as Differential Privacy (DP) and Homomorphic Encryption (HE). The framework leverages Deep Neural Networks (DNNs), Federated Learning (FL), and Reinforcement Learning (RL) for intelligent orchestration and anomaly detection, while blockchain technology ensures decentralized trust management. The evaluation using NS-3, SUMO, and Mininet demonstrates significant improvements in latency reduction, anomaly detection accuracy, privacy leakage minimization, and network adaptability compared to baseline NFV approaches. The proposed framework highlights the role of bio-inspired AI in enabling privacy-aware, secure, and resilient NFV for next-generation autonomous driving systems.
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