School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
School of Electronic Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Sensors (Basel). 2023 May 14;23(10):4751. doi: 10.3390/s23104751.
In cognitive radio systems, cooperative spectrum sensing (CSS) can effectively improve the sensing performance of the system. At the same time, it also provides opportunities for malicious users (MUs) to launch spectrum-sensing data falsification (SSDF) attacks. This paper proposes an adaptive trust threshold model based on a reinforcement learning (ATTR) algorithm for ordinary SSDF attacks and intelligent SSDF attacks. By learning the attack strategies of different malicious users, different trust thresholds are set for honest and malicious users collaborating within a network. The simulation results show that our ATTR algorithm can filter out a set of trusted users, eliminate the influence of malicious users, and improve the detection performance of the system.
在认知无线电系统中，协作频谱感知（CSS）可以有效地提高系统的感知性能。同时，它也为恶意用户（MUs）提供了发起频谱感知数据伪造（SSDF）攻击的机会。本文提出了一种基于强化学习（ATTR）算法的自适应信任门限模型，用于普通 SSDF 攻击和智能 SSDF 攻击。通过学习不同恶意用户的攻击策略，为网络内协作的诚实和恶意用户设置不同的信任门限。仿真结果表明，我们的 ATTR 算法可以筛选出一组可信用户，消除恶意用户的影响，提高系统的检测性能。
