This paper addresses the challenge of enhancing cybersecurity in
Blockchain-based Internet of Things (BIoTs) systems, which are increasingly
vulnerable to sophisticated cyberattacks. It introduces an AI-powered system
model for the dynamic deployment of honeypots, utilizing an Intrusion Detection
System (IDS) integrated with smart contract functionalities on IoT nodes. This
model enables the transformation of regular nodes into decoys in response to
suspicious activities, thereby strengthening the security of BIoT networks. The
paper analyses strategic interactions between potential attackers and the
AI-enhanced IDS through a game-theoretic model, specifically Bayesian games.
The model focuses on understanding and predicting sophisticated attacks that
may initially appear normal, emphasizing strategic decision-making, optimized
honeypot deployment, and adaptive strategies in response to evolving attack
patterns.

使用具有智能合约功能的入侵检测系统，将普通节点转化为诱饵来应对可疑活动，从而加强区块链物联网网络的安全性；利用贝叶斯博弈的策略分析潜在攻击者与增强型入侵检测系统之间的交互作用，重点关注并预测外部攻击，并强调在面对不断演变的攻击模式时的策略决策、优化的诱饵部署和自适应策略。

基于区块链的物联网系统蜜罐的战略部署

Strategic Deployment of Honeypots in Blockchain-based IoT Systems

This study proposes a framework to enhance privacy in Blockchain-based
Internet of Things (BIoT) systems used in the healthcare sector. The framework
addresses the challenge of leveraging health data for analytics while
protecting patient privacy. To achieve this, the study integrates Differential
Privacy (DP) with Federated Learning (FL) to protect sensitive health data
collected by IoT nodes. The proposed framework utilizes dynamic personalization
and adaptive noise distribution strategies to balance privacy and data utility.
Additionally, blockchain technology ensures secure and transparent aggregation
and storage of model updates. Experimental results on the SVHN dataset
demonstrate that the proposed framework achieves strong privacy guarantees
against various attack scenarios while maintaining high accuracy in health
analytics tasks. For 15 rounds of federated learning with an epsilon value of
8.0, the model obtains an accuracy of 64.50%. The blockchain integration,
utilizing Ethereum, Ganache, Web3.py, and IPFS, exhibits an average transaction
latency of around 6 seconds and consistent gas consumption across rounds,
validating the practicality and feasibility of the proposed approach.

提出一种框架来增强基于区块链的物联网（BIoT）系统在医疗保健领域中的隐私保护，通过集成差分隐私（DP）和联邦学习（FL）来保护物联网节点收集的敏感健康数据，并利用动态个性化和自适应噪声分布策略平衡隐私和数据效用，同时通过区块链技术确保模型更新的安全透明聚合和存储，实验证明该框架在各种攻击场景下具有强大的隐私保证，并在健康分析任务中保持高准确性，平均每轮联邦学习花费 6 秒左右的交易延迟，注重性能和可行性。