基于Eclat算法的认知无线网络能量检测

Fan Jin, V. Varadharajan, U. Tupakula
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引用次数: 4

摘要

认知无线电(CR)可以通过机会性地利用许可频谱来提高频谱的利用率。来自所有CR节点的感知报告被发送到融合中心(FC),融合中心汇总这些报告并根据一些决策规则对PU的存在做出决策。这种协同感知机制构成了任何集中式CRN的基础。然而,这种协同感知机制为隐藏在合法用户中的恶意用户(mu)提供了更多的机会来发动频谱感知数据伪造(SSDF)攻击。在SSDF攻击中,一些恶意用户故意向FC报告错误的局部感知结果,从而破坏全局决策过程。为了减轻SSDF攻击,本文提出了一种基于Eclat算法的恶意节点串谋检测策略。仿真结果表明,在存在SSDF攻击的情况下,该方案的感知性能优于传统的基于多数的投票决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Eclat Algorithm Based Energy Detection for Cognitive Radio Networks
Cognitive radio (CR) can improve the utilization of the spectrum by making use of licensed spectrum in an opportunistic manner. The sensing reports from all the CR nodes are sent to a Fusion Centre (FC) which aggregates these reports and takes decision about the presence of the PU, based on some decision rules. Such a collaborative sensing mechanism forms the foundation of any centralised CRN. However, this collaborative sensing mechanism provides more opportunities for malicious users (MUs) hiding in the legal users to launch spectrum sensing data falsification (SSDF) attacks. In an SSDF attack, some malicious users intentionally report incorrect local sensing results to the FC and disrupt the global decision-making process. To mitigate SSDF attacks, an Eclat algorithm based detection strategy is proposed in this paper for finding out the colluding malicious nodes. Simulation results show that the sensing performance of the scheme is better than the traditional majority based voting decision in the presence of SSDF attacks.
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