论文标题
通过稀疏编码在认知无线电中的主要用户仿真和干扰攻击检测
Primary User Emulation and Jamming Attack Detection in Cognitive Radio via Sparse Coding
论文作者
论文摘要
认知无线电是一台智能和适应性无线电，通过其机会主义共享来改善频谱的利用。但是，它本质上容易受到降低频谱利用率的主要用户仿真和干扰攻击的影响。在本文中，提出了一种用于检测主要用户仿真和阻塞攻击的算法。所提出的算法基于在通道依赖性词典上接收信号的稀疏编码。更具体地说，根据该词典的稀疏编码中的收敛模式用于区分光谱孔，合法的主要用户，模拟器或干扰器。决策过程是作为基于机器学习的分类操作进行的。广泛的数值实验表明，所提出的算法在检测上述攻击方面的有效性很高。这是根据混乱矩阵质量指标对此进行验证的。此外，根据接收器操作特征曲线和这些曲线下的区域，所提出的算法被证明优于基于能量检测的机器学习技术
Cognitive radio is an intelligent and adaptive radio that improves the utilization of the spectrum by its opportunistic sharing. However, it is inherently vulnerable to primary user emulation and jamming attacks that degrade the spectrum utilization. In this paper, an algorithm for the detection of primary user emulation and jamming attacks in cognitive radio is proposed. The proposed algorithm is based on the sparse coding of the compressed received signal over a channel-dependent dictionary. More specifically, the convergence patterns in sparse coding according to such a dictionary are used to distinguish between a spectrum hole, a legitimate primary user, and an emulator or a jammer. The process of decision-making is carried out as a machine learning-based classification operation. Extensive numerical experiments show the effectiveness of the proposed algorithm in detecting the aforementioned attacks with high success rates. This is validated in terms of the confusion matrix quality metric. Besides, the proposed algorithm is shown to be superior to energy detection-based machine learning techniques in terms of receiver operating characteristics curves and the areas under these curves
