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强大的基于学习的检测与成本控制和拜占庭式缓解
Chen Zhong1, M Cenk Gursoy1, Senem Velipasalar1
1Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY 13244, USA.
Sensors (Basel, Switzerland)
|January 10, 2026
概括
本研究介绍了用于传感器调度的软行为体-批判性 (SAC) 深度强化学习 (DRL) 框架和用于检测拜占庭传感器的生成对抗网络 (GAN). 综合方法提高了状态估计的可靠性和速度,同时在杂的环境中降低成本.
科学领域:
- 控制系统 控制系统
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 状态估计和检测受到杂的传感器数据,通信错误和探测成本的挑战.
- 拜占庭式攻击对传感器网络和国家检测系统的完整性构成重大威胁.
研究的目的:
- 开发一个协调的框架,使用软行为体-关键 (SAC) 深度强化学习 (DRL) 和生成对抗网络 (GAN) 进行动态传感器调度和拜占庭传感器检测.
- 快速可靠地检测随机系统的状态,同时最大限度地降低检测成本,即使在不利的条件下.
主要方法:
- 提出了一个SAC-DRL框架,用于动态调度传感器和顺序检测系统状态.
- 一个基于GAN的框架旨在识别拜占庭传感器,与SAC-DRL代理协调运行.
- 使用诸如检测准确度,停止时间和总探测成本等指标来评估性能.
主要成果:
- SAC-DRL框架在不完美的已知环境中展示了行动选择的灵活性和有效性,利用了最大的策略.
- 在具有挑战性的场景中实现稳定的性能,包括干扰攻击,未知的噪音水平和高传感成本.
- 基于GAN的方法与SAC-DRL代理相结合,在拜占庭攻击的情况下显著提高了可靠性和准确性.
结论:
- 拟议的协调SAC-DRL和GAN框架为复杂,杂和敌对环境中的状态估计和检测提供了一个强大的解决方案.
- 软演员-关键算法非常适合在不确定的条件下进行动态决策,因为它们固有的概率方法.
- 拜占庭传感器检测的整合提高了国家估计系统的整体可靠性和性能.
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