在区块链中使用强化学习和在对抗性环境中验证的自适应共识优化.
Rommel Gutierrez1, William Villegas-Ch1, Jaime Govea1
1Escuela de Ingeniería en Ciberseguridad, FICA, Universidad de Las Américas, Quito, Ecuador.
Frontiers in artificial intelligence
|October 16, 2025
概括
这项研究介绍了一个自适应的区块链共识架构,使用强化学习来增强安全性和效率. 该系统提高了吞吐量,减少了延迟,并在不利的网络条件下降低了能源消耗.
科学领域:
- 区块链技术 区块链技术
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 现代区块链网络面临着复杂性和分散性的挑战,在不利的条件下限制了传统的共识协议.
- 现有系统与实时异常作斗争,如Sybil攻击和网络拥堵,导致性能下降和安全漏洞.
- 缺乏自主政策调整机制阻碍了适应能力,特别是在资源有限的边缘计算环境中.
研究的目的:
- 为区块链网络提出适应性共识架构.
- 增强对抗场景和动态网络条件的弹性.
- 提高分散系统的效率,安全性和能源消耗.
主要方法:
- 集成基于图形的近接政策优化 (PPO) 强化学习代理.
- 在真实流量和合成对抗行为混合数据集上训练代理.
- 在具有多个威胁载体的压力测试环境中进行评估,包括Sybil攻击和网络拥堵.
主要成果:
- 在高负载条件下保持稳定的吞吐量 (TPS) 和减少34%的共识延迟.
- 在Sybil和节点崩场景中实现了高检测率 (DR > 0.90,FPR < 0.10).
- 在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在稳定的收和适应的情况下,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵和易发生事故的场景中,在高拥堵的场景中.
结论:
- 拟议的自适应共识架构有效地提高了区块链性能和安全性.
- 强化学习的整合提供了强大的适应动态和对抗性的网络条件.
- 该系统显示了在边缘计算和超越现实世界的部署的巨大潜力.
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