Mobile cognitive radio networks provide a new platform to implement and adapt wireless cellular communications, increasing the use of the electromagnetic spectrum by using it when the primary user is not using it and providing cellular service to secondary users. In these networks, there exist vulnerabilities that can be exploited, such as the malicious primary user emulation (PUE), which tries to imitate the primary user signal to make the cognitive network release the used channel, causing a denial of service to secondary users. We propose a support vector machine (SVM) technique, which classifies if the received signal is a primary user or a malicious primary user emulation signal by using the signal-to-noise ratio (SNR) and Rényi entropy of the energy signal as an input to the SVM. This model improves the detection of the malicious attacker presence in low SNR without the need for a threshold calculation, which can lead to false detection results, especially in orthogonal frequency division multiplexing (OFDM) where the threshold is more difficult to estimate because the signal limit values are very close in low SNR. It is implemented on a software-defined radio (SDR) testbed to emulate the environment of mobile system modulations, such as Gaussian minimum shift keying (GMSK) and OFDM. The SVM made a previous learning process to allow the SVM system to recognize the signal behavior of a primary user in modulations such as GMSK and OFDM and the SNR value, and then the received test signal is analyzed in real-time to decide if a malicious PUE is present. The results show that our solution increases the detection probability compared to traditional techniques such as energy or cyclostationary detection in low SNR values, and it detects malicious PUE signal in MCRN.

中文翻译：

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基于支持向量机的移动认知无线电网络中使用软件定义无线电的恶意主要用户仿真检测

移动认知无线电网络提供了一个新的平台来实现和调整无线蜂窝通信，通过在主要用户不使用电磁频谱时使用电磁频谱并向其提供蜂窝服务来增加电磁频谱的使用。在这些网络中，存在可以利用的漏洞，例如恶意的主要用户仿真（PUE），它试图模仿主要用户信号以使认知网络释放已使用的通道，从而导致对次要用户的拒绝服务。我们提出了一种支持向量机（SVM）技术，该技术通过使用能量信号的信噪比（SNR）和Rényi熵作为输入来对接收到的信号是主要用户还是恶意主要用户仿真信号进行分类到SVM。此模型无需阈值计算就可以在低SNR的情况下改进对恶意攻击者存在的检测，这可能会导致错误的检测结果，尤其是在正交频分复用（OFDM）中，由于信号限制，阈值更难估计在低SNR中，这些值非常接近。它在软件定义无线电（SDR）测试平台上实现，以模拟移动系统调制的环境，例如高斯最小频移键控（GMSK）和OFDM。SVM进行了先前的学习过程，以使SVM系统能够识别主要用户在GMSK和OFDM等调制方式下的信号行为以及SNR值，然后对接收到的测试信号进行实时分析，以确定是否为恶意软件。存在PUE。