Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security

Edge computing-based Next-Generation Wireless Networks (NGWN)-IoT offer enhanced bandwidth capacity for large-scale service provisioning but remain vulnerable to evolving cyber threats. Existing intrusion detection and prevention methods struggle to counter dynamically evolving cyber threats, as they often rely on static signatures or isolated anomaly detection techniques. These approaches lack adaptability to novel attack vectors, exhibit high false negative rates, and fail to integrate realtime threat intelligence, leaving NGWN-IoT systems vulnerable to sophisticated adversaries. We propose a dynamic approach to detect and prevent attacks. First, blockchain-based authentication utilizes the Deoxys Authentication Algorithm (DAA) to verify the legitimacy of IoT devices before data transmission. Next, a bi-stage intrusion detection system is introduced, where the first stage utilizes signature-based detection via an Improved Random Forest (IRF) algorithm. In contrast, the second stage applies feature-based anomaly detection using a Diffusion Convolution Recurrent Neural Network (DCRNN). To ensure Quality of Service (QoS) and maintain Service Level Agreements (SLA), trust-aware service migration is performed using Heap-Based Optimization (HBO). Additionally, on-demand virtual High-Interaction honeypots deceive attackers and extract attack patterns, which are securely stored using the Bimodal Lattice Signature Scheme (BLISS) to enhance signature-based Intrusion Detection Systems (IDS). The proposed framework is implemented in the NS3 simulation environment and evaluated against existing methods across multiple performance metrics, including accuracy, attack detection rate, false negative rate, precision, recall, ROC curve, memory usage, CPU usage, and execution time. Experimental results demonstrate that the framework significantly outperforms existing approaches, reinforcing the security of NGWN-enabled IoT ecosystems.