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Sequential fusion to defend against sensing data falsification attack for cognitive Internet of Things

机译:依次融合来防御传感数据的认知互联网伪造攻击

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摘要

Internet of Things (IoT) is considered the future network to support wireless communications. To realize an IoT network, sufficient spectrum should be allocated for the rapidly increasing IoT devices. Through cognitive radio, unlicensed IoT devices exploit cooperative spectrum sensing (CSS) to opportunistically access a licensed spectrum without causing harmful interference to licensed primary users (PUs), thereby effectively improving the spectrum utilization. However, an open access cognitive IoT allows abnormal IoT devices to undermine the CSS process. Herein, we first establish a hard‐combining attack model according to the malicious behavior of falsifying sensing data. Subsequently, we propose a weighted sequential hypothesis test (WSHT) to increase the PU detection accuracy and decrease the sampling number, which comprises the data transmission status‐trust evaluation mechanism, sensing data availability, and sequential hypothesis test. Finally, simulation results show that when various attacks are encountered, the requirements of the WSHT are less than those of the conventional WSHT for a better detection performance.
机译:事物互联网(物联网)被认为是未来的支持无线通信的网络。为了实现IOT网络,应该为快速增加的物联网设备分配足够的频谱。通过认知无线电,未经许可的IOT设备利用协作频谱感测(CSS)来机会访问许可频谱,而不会对许可的主要用户(PU)产生有害干扰,从而有效提高频谱利用率。但是,开放访问认知物联网允许异常的物联网设备以破坏CSS过程。这里,我们首先根据伪造的感测数据的恶意行为建立一个硬组合攻击模型。随后,我们提出了一种加权顺序假设测试(WSHT)来增加PU检测精度并降低采样号,包括数据传输状态信任评估机制,感测数据可用性和顺序假设测试。最后,仿真结果表明,当遇到各种攻击时,WSHT的要求小于传统WSHT的要求,以更好的检测性能。

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