基于强化学习的自适应信任值模型,用于合作频谱传感
Gang Xie1, Xincheng Zhou2, Jinchun Gao3
1School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
本研究介绍了一种使用强化学习的自适应信任值模型,用于打击合作频谱传感系统中的频谱传感数据伪造攻击. 该算法有效地识别和过恶意用户,提高整体系统检测性能.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 网络安全 网络安全
背景情况:
- 合作频谱传感 (CSS) 提高了认知无线电系统的性能.
- 恶意用户 (MUs) 利用CSS发起频谱传感数据伪造 (SSDF) 攻击.
- 现有的方法很难有效地区分诚实和恶意用户.
研究的目的:
- 为检测SSDF攻击提出一个自适应的信任值模型 (ATTR).
- 加强对恶意活动的合作频谱传感的弹性.
- 为了提高在存在普通和智能SSDF攻击时的整体检测性能.
主要方法:
- 开发了一个基于强化学习的自适应信任值模型 (ATTR).
- 该ATTR算法学习恶意用户的攻击策略.
- 对合作用户实施了动态信任值调整.
主要成果:
- 该ATTR算法成功过出可信用户.
- 恶意用户对传感性能的影响被消除了.
- 系统的检测性能得到了显著的改善.
结论:
- 拟议的ATTR算法有效地减轻了SSDF在CSS中的攻击.
- 强化学习提供了一个适应性的方法来管理在动态环境中的信任.
- ATTR提高了认知无线电系统的可靠性和安全性.
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