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

arXiv: https://arxiv.org/abs/2504.16226 · HTML: https://mlsanity.com/paper/2504.16226/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 66 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [A Survey of Honeypots and Honeynets for Internet of Things, Industrial Internet of Things, and Cyber-Physical Systems](https://mlsanity.com/paper/2108.02287/) (2021)
- [Machine Learning based Anomaly Detection for 5G Networks](https://mlsanity.com/paper/2003.03474/) (2020)
- [A Blockchain-Based Audit Mechanism for Trust and Integrity in IoT-Fog Environments](https://mlsanity.com/paper/2405.00844/) (2024)

## Built on by (most cited first, 1 of 1)

- [Autonomous AI-based Cybersecurity Framework for Critical Infrastructure: Real-Time Threat Mitigation](https://mlsanity.com/paper/2507.07416/) (2025)

## Abstract (arXiv)

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, and execution time. Experimental results demonstrate that the framework significantly outperforms existing approaches, reinforcing the security of NGWN-enabled IoT ecosystems