在PoW区块链中适应性威胁缓解 (第二部分):一种深度强化学习方法来对抗逃避敌人
1Institute of Computing Science, Poznan University of Technology, 60-965 Poznań, Poland.
Sensors (Basel, Switzerland)
|February 27, 2026
概括
本研究介绍了使用深度强化学习 (DRL) 的自适应式区块链安全框架. DRL代理动态调整防御,使攻击无利可图,并提高网络抵御不断变化的威胁的弹性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 静态区块链安全机制与适应性对手作斗争.
- 智能攻击者可以绕过传统的,不进化的防御系统.
- 在分散的网络中,急需动态安全解决方案.
研究的目的:
- 为区块链提出下一代适应性安全框架.
- 利用深度强化学习 (DRL) 来进行动态安全参数调整.
- 增强区块链对智能和不断变化的网络威胁的弹性.
主要方法:
- 开发了一个DRL代理来根据网络条件动态调整安全参数.
- 使用基于代理的奖励函数训练代理,优化网络稳定性.
- 对适应性对手和新型攻击变体 (零日) 的框架进行了评估.
主要成果:
- 通过DRL增强的框架使得针对可适应对手的攻击变得无利可图 (-42±13%).
- 静态模型对适应性对手失败,显示了有利可图的攻击 (+65±22%).
- 与其他AI方法相比,它获得了0.95±0.02的优异F1得分,并在24小时内抑制了零日攻击.
结论:
- 深度强化学习为自适应式区块链安全提供了强大的解决方案.
- 拟议的框架为应对复杂的网络威胁提供了增强的弹性和利能力缓解.
- 这项工作为智能,适应性和安全的未来去中心化网络奠定了基础.
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