认知无线电网络主用户仿真攻击检测与定位的粒子群优化方法
Particle Swarm Optimization Approaches for Primary User Emulation Attack Detection and Localization in Cognitive Radio Networks
摘要：
主用户仿真攻击（PUEA）是认知无线电网络（CRN）中常见的威胁之一，在该问题中，攻击者模仿主用户（PU）信号欺骗其他次用户（SU），使其离开PU分配的频谱中的空白空间（空闲空间）。在本文中，PUEA检测和定位使用到达时间差（TDOA）定位技术。提出了一种基于粒子群优化（PSO）算法的TDOA测量代价函数求解方法。通过改变标准PSO的惯性权重和加速度常数等参数，开发了PSO变体。这些方法的收敛速度和处理时间与标准的粒子群算法进行了比较。本文首次提出了一种适合于定位问题的粒子群优化算法的设计研究，将被认为是一个很好的指导应用优化算法在无线定位技术。均方误差（MSE）和累积分布函数（CDF）被用来作为评估指标来衡量所提出的算法的准确性。仿真结果表明，所提出的粒子群算法具有更高的精度和更快的收敛速度比标准粒子群算法和泰勒级数估计（TSE）。
英文摘要：
The primary user emulation attack (PUEA) is one of the common threats in cognitive radio networks (CRNs), in this problem, an attacker mimics the Primary User (PU) signal to deceive other secondary users (SUs) to make them leave the white spaces (free spaces) in the spectrum assigned by the PU. In this paper, the PUEA is detected and localized using the Time-Difference-Of-Arrival (TDOA) localization technique. Particle Swarm Optimization (PSO) algorithms are proposed to solve the cost function of TDOA measurements. The PSO variants are developed by changing the parameters of the standard PSO such as inertia weight and acceleration constants. These approaches are presented and compared with the standard PSO in terms of convergence speed and processing time. This paper presents the first study of designing a PSO algorithm suitable for the localization problem and will be considered as a good guidance for applying the optimization algorithms in wireless positioning techniques. Mean square error (MSE) and cumulative distribution function (CDF) are used as the evaluation metrics to measure the accuracy of the proposed algorithms. Simulation results show that the proposed PSO approaches provide higher accuracy and faster convergence than the standard PSO and the Taylor series estimation (TSE).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
