论文标题
预印本:使用RF-DNA指纹在瑞利褪色条件下对OFDM发射机进行分类
Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions
论文作者
论文摘要
物联网(IoT)是一个能够与物理世界和计算机系统进行交互的Internet连接设备的集合。据估计,到2020年,物联网将由大约五十亿个设备组成。除了纯粹的数字外,对物联网安全的需求也会加剧以下事实,即许多边缘设备使用弱弱到没有通信链接的加密。据估计,将近70%的物联网设备没有使用任何形式的加密。先前的研究表明,使用特定的发射极识别(SEI),一种物理层技术,作为增强比特安全机制(例如加密)的一种手段。这里介绍的工作集成了一种基于Nelder-Mead的方法,用于在SEI方法被称为RF-DNA指纹识别之前估算瑞利褪色通道系数。评估该估计器的性能是否有降解的信噪比,并与最小平方和最小平方误差通道估计器进行了比较。此外,这项工作使用RF-DNA指纹呈现了分类结果,这些指纹从接受的信号中提取,这些信号使用最小平均误差(MMSE)均衡进行了雷利褪色通道校正。这项工作还使用由Gabor系数和两个分类器的归一化幅度平方和相位响应产生的RF-DNA指纹进行无线电歧视。使用由两个和五个路径组成的瑞利褪色通道,对四个802.11a Wi-Fi无线电的歧视对于信噪比为18和21 dB或更高的信噪比的平均正确分类为90%或更高。
The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that the IoT will consist of approximately fifty billion devices by the year 2020. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanism such as encryption. The work presented here integrates a Nelder-Mead based approach for estimating the Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA fingerprinting. The performance of this estimator is assessed for degrading signal-to-noise ratio and compared with least square and minimum mean squared error channel estimators. Additionally, this work presents classification results using RF-DNA fingerprints that were extracted from received signals that have undergone Rayleigh fading channel correction using Minimum Mean Squared Error (MMSE) equalization. This work also performs radio discrimination using RF-DNA fingerprints generated from the normalized magnitude-squared and phase response of Gabor coefficients as well as two classifiers. Discrimination of four 802.11a Wi-Fi radios achieves an average percent correct classification of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a Rayleigh fading channel comprised of two and five paths, respectively.