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 provide limited security as adversaries continually adapt
their attack strategies. We propose a dynamic attack detection and prevention
approach to address this challenge. First, blockchain-based authentication uses
the Deoxys Authentication Algorithm (DAA) to verify IoT device legitimacy
before data transmission. Next, a bi-stage intrusion detection system is
introduced: the first stage uses 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, Und
execution time. Experimental results demonstrate that the framework
significantly outperforms existing approaches, reinforcing the security of
NGWN-enabled IoT ecosystems

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