协同频谱感知拜占庭攻击的恶意利用

Jipeng Gan, Jun Wu, Pei Li, Zehao Chen, Zehao Chen, Jia Zhang, Jian-Duo He
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引用次数: 0

摘要

协同频谱感知是认知无线电提高频谱感知性能的关键。然而,这种合作模式受到拜占庭式攻击的威胁。为了保证云存储系统的安全性和能效,本文提出了一种恶意利用算法。首先,我们根据二级用户(su)的历史性能区分正常用户(NUs)和恶意用户(mu)。与以往的研究不同,我们创新性地利用了来自微信号的传感信息,提高了CSS的检测性能。此外,我们在数据融合中选择特定的SUs而不是所有的SUs,这减少了SUs提交给融合中心(FC)的样本数量。最后,我们进一步引入了一个顺序差分机制,该机制大大减少了样本,以提高CSS的EE。最后,通过数值仿真验证了算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Malicious Exploitation of Byzantine Attack for Cooperative Spectrum Sensing
Cooperative spectrum sensing (CSS) is crucial for cognitive radio (CR) to improve spectrum sensing performance. However, the cooperative paradigm is threatened by Byzantine attacks. To ensure the security and energy efficiency (EE) of CSS, in this paper, we propose a malicious exploitation algorithm. Firstly, we distinguish normal users (NUs) from malicious users (MUs) based on the historical performance of secondary users (SUs). Unlike most previous studies, we innovatively improve CSS detection performance by exploiting sensing information from MUs. In addition, we select specific SUs instead of all SUs in data fusion, which reduces the number of samples submitted by SUs to the fusion center (FC). Finally, we further introduce a sequential differential mechanism that substantially reduces samples to improve the EE of CSS. Finally, the numerical simulation results validate the effectiveness of our proposed algorithm.
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