Blockchain-Integrated Deep Intelligence for Distributed Wireless Network Protection

Abstract

Distributed wireless networks are increasingly exposed to security threats such as data tampering, spoofing attacks, node compromise, and unauthorized access. Traditional security mechanisms struggle to provide scalable, real-time, and tamper-resistant protection in dynamic network environments. To address these challenges, this study proposes a Blockchain-Integrated Deep Intelligence framework for Distributed Wireless Network Protection (BDI-DWNP). The proposed model integrates blockchain technology for decentralized security, immutability, and trust management with deep learning-based intrusion detection for intelligent threat classification. The blockchain layer ensures secure transaction validation and node authentication, while the deep learning module analyzes network traffic patterns to detect anomalies and malicious behavior. The system is evaluated using network intrusion datasets, and performance is measured using accuracy, precision, recall, F1-score, and detection latency. Experimental results demonstrate that the proposed framework significantly improves detection accuracy and enhances network security compared to conventional intrusion detection systems. The framework is suitable for IoT networks, wireless sensor networks, and large-scale distributed communication systems.