In modern wireless systems such as ZigBee, sensitive information which is produced by the network is transmitted through different wired or wireless nodes. Providing the requisites of communication between diverse communication system types, such as mobiles, laptops, and desktop computers, does increase the risk of being attacked by outside nodes. Malicious (or unintentional) threats, such as trying to obtain unauthorized accessibility to the network, increase the requirements of data security against the rogue devices trying to tamper with the identity of authorized devices. In such manner, focusing on Radio Frequency Distinct Native Attributes (RF-DNA) of features extracted from physical layer responses (referred to as preambles) of ZigBee devices, a dataset of distinguishable features of all devices can be produced which can be exploited for the detection and rejection of spoofing/rogue devices. Through this procedure, distinction of devices manufactured by the different/same producer(s) can be realized resulting in an improvement of classification system accuracy. The two most challenging problems in initiating RF-DNA are (1) the mechanism of features extraction in the generation of a dataset in the most effective way for model classification and (2) the design of an efficient model for device discrimination of spoofing/rogue devices. In this paper, we analyze the physical layer features of ZigBee devices and present methods based on deep learning algorithms to achieve high classification accuracy, based on wavelet decomposition and on the autoencoder representation of the original dataset.

在诸如ZigBee的现代无线系统中，网络产生的敏感信息是通过不同的有线或无线节点传输的。提供各种通信系统类型（例如，移动设备，便携式计算机和台式计算机）之间的通信需求，确实增加了受到外部节点攻击的风险。恶意（或无意）威胁，例如试图获得对网络的未授权访问，增加了针对恶意设备的数据安全性要求，这些恶意设备试图篡改授权设备的身份。以这种方式，重点关注从ZigBee设备的物理层响应（称为前同步码）中提取的特征的射频独特本机属性（RF-DNA），可以生成所有设备的明显特征的数据集，这些数据集可用于检测和拒绝欺骗/欺诈设备。通过该程序，可以实现由不同/相同生产者制造的设备的区别，从而导致分类系统精度的提高。引发RF-DNA的两个最具挑战性的问题是：（1）以最有效的模型分类方式在数据集中生成特征提取的机制，以及（2）设计用于欺骗/欺诈的设备判别的有效模型设备。在本文中，我们分析了ZigBee设备的物理层特征，并提出了基于深度学习算法的方法，以实现较高的分类精度，