用区块链与动态蜜罐提升物联网安全，防攻击更智能。
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security
- 结合区块链认证与双阶段检测，动态识别攻击。
- 检测准确率超95%，误报率低于3%，显著优于传统方法。
- 适合研究物联网安全、智能防御系统的开发者与学者。
基于边缘计算的下一代无线网络（NGWN）-物联网在提供大容量带宽服务的同时，仍面临持续演化的网络威胁。现有入侵检测与防护方法难以应对攻击者策略的快速变化。本文提出一种动态攻击检测与防护框架：首先采用基于区块链的去氧认证算法（DAA）验证设备合法性；第一阶段使用改进随机森林（IRF）进行签名检测，第二阶段通过扩散卷积循环神经网络（DCRNN）实现特征级异常检测；为保障服务质量（QoS）与服务等级协议（SLA），引入基于堆优化（HBO）的信任感知服务迁移机制；此外，按需部署虚拟高交互蜜罐诱骗攻击者并提取攻击模式，利用双模格签名方案（BLISS）安全存储以增强签名检测系统。框架在NS3仿真环境中评估，对比多项指标（准确率、检测率、误报率、精度、召回率、ROC曲线、内存、CPU、执行时间）。实验表明，该框架显著优于现有方法，有效强化了NGWN支持的物联网生态安全。
原文摘要 · Abstract (English)
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
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