对比三种强化学习在区块链物联网中的抗攻击能力，发现多智能体学习最有效。
Adaptive Trust Consensus for Blockchain IoT: Comparing RL, DRL, and MARL Against Naive, Collusive, Adaptive, Byzantine, and Sleeper Attacks
- 用RL、DRL和MARL三种方法应对五类攻击，评估其防御效果。
- 多智能体学习在共谋攻击下F1达0.85，远超其他方法。
- 时间延迟投毒攻击让所有模型性能暴跌至0.11-0.16，威胁极大。
针对区块链物联网网络中复杂的恶意攻击，本文提出一种基于信任的委托共识框架，结合全同态加密（FHE）与基于属性的访问控制（ABAC），实现隐私保护下的策略评估，并集成基于学习的防御机制。系统性对比了三种强化学习方法：表格Q学习（RL）、深度强化学习（带双DQN的分层结构，DRL）以及多智能体强化学习（MARL），应对五类攻击：无害恶意攻击（NMA）、共谋谣言攻击（CRA）、自适应对抗攻击（AAA）、拜占庭故障注入（BFI）及时间延迟投毒攻击（TDP）。在16节点模拟物联网网络上的实验显示，多智能体学习在共谋攻击下表现最优（F1=0.85），显著优于DRL（0.68）和RL（0.50）；在自适应攻击中，DRL与MARL均实现完美检测（F1=1.00），而RL失败（F1=0.50）。所有方法在拜占庭攻击下均成功防御（F1=1.00）。最关键的是，时间延迟投毒攻击在“休眠激活”后使所有方法的F1降至0.11–0.16，暴露出信任构建型对手的巨大威胁。结果表明，协同多智能体学习在抵御复杂信任操纵攻击方面具有显著优势。
原文摘要 · Abstract (English)
Securing blockchain-enabled IoT networks against sophisticated adversarial attacks remains a critical challenge. This paper presents a trust-based delegated consensus framework integrating Fully Homomorphic Encryption (FHE) with Attribute-Based Access Control (ABAC) for privacy-preserving policy evaluation, combined with learning-based defense mechanisms. We systematically compare three reinforcement learning approaches -- tabular Q-learning (RL), Deep RL with Dueling Double DQN (DRL), and Multi-Agent RL (MARL) -- against five distinct attack families: Naive Malicious Attack (NMA), Collusive Rumor Attack (CRA), Adaptive Adversarial Attack (AAA), Byzantine Fault Injection (BFI), and Time-Delayed Poisoning (TDP). Experimental results on a 16-node simulated IoT network reveal significant performance variations: MARL achieves superior detection under collusive attacks (F1=0.85 vs. DRL's 0.68 and RL's 0.50), while DRL and MARL both attain perfect detection (F1=1.00) against adaptive attacks where RL fails (F1=0.50). All agents successfully defend against Byzantine attacks (F1=1.00). Most critically, the Time-Delayed Poisoning attack proves catastrophic for all agents, with F1 scores dropping to 0.11-0.16 after sleeper activation, demonstrating the severe threat posed by trust-building adversaries. Our findings indicate that coordinated multi-agent learning provides measurable advantages for defending against sophisticated trust manipulation attacks in blockchain IoT environments.
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