基于区块链和深度强化学习的共享车辆数据的动态信任管理和防御决策系统的设计
Jinxiang Chen1, Yan Li1, Jiaxing Deng1
1Shenzhen Ruipengfei Mold Co., Ltd, Shenzhen, 518000, China.
Scientific reports
|July 22, 2025
概括
这项研究引入了共享车辆数据的新系统,将区块链和深度强化学习 (DRL) 结合起来,以加强信任管理. 综合方法提高了分布式数据分析的准确性,检测率和响应时间.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 在共享车辆数据系统中的信任管理面临重大挑战.
- 现有的集中式系统在安全性,稳定性和高效决策方面扎.
- 需要创新的方法来增强分布式环境中的信任和数据完整性.
研究的目的:
- 开发和评估共享车辆的新型数据分析系统.
- 将基于区块链的分布式信任管理与深度强化学习 (DRL) 整合在一起.
- 解决用户信任评估,行为建模和防御战略实施方面的挑战.
主要方法:
- 使用贝叶斯统计和历史数据进行可信度估计的用户信任评估模型.
- 实现区块链技术以分离数据和信任链,确保分布式存储和强大的管理.
- 一个马尔科夫决策过程框架,用于建模共享车辆服务和用户行为,利用深度Q网络 (DQN) 算法进行最佳的防御策略.
主要成果:
- 与集中式方法相比,基于区块链的信任评估提高了约16%的准确性.
- 与Q-learning相比,基于DQN的系统在平均系统奖励中显示出超过20%的绩效增长.
- 恶意行为检测率达到约93% (超过15%的改善),服务响应时间减少了11%以上.
结论:
- 拟议的系统有效地解决了共享车辆数据系统中的信任管理和决策挑战.
- 将区块链的分散功能与DRL的优化潜力相结合,提供了一个可扩展和高效的解决方案.
- 综合方法提高了复杂的分布式数据分析场景的安全性,可靠性和运行速度.
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