## 1 Introduction  
With the study advances on IoT technologies, the IoT devices have a wide application in our daily life, e.g., a smart city in which the IoT devices belonging to different application contexts cooperate for providing services related to e-health, smart factories, energy and traffic management [1; 2]. According to Gartner, IoT product and service suppliers will generate incremental revenue exceeding $300 billion, mostly in services, in 2020 [3]. Additionally, in the emerging domain of the IoT, cyberphysical IoT devices has become a novel type of digital resources, which is a physical object augmented with sensing/actuation, processing, storing, and networking capabilities [4]. All these devices provide a set of physical and digital services to humans, machines, or digital systems. However, the development of IoT devices brings not only lots of conveniences but also enormous problems for the security. Several papers (e.g., [5; 6; 7; 8; 9]) have studied related security issues. And a recent study by Hewlett Packard revealed that 70% of the most commonly used IoT devices contained serious vulnerabilities [10]. Once they have been attacked, many of the sensitive data (privacy for medical data, personal routine data, etc.) may be revealed to unknown sources, which will result in identity theft. For example, in 2016, the hackers generated a large number of DDoS attacks on the Dynamic Domain Name System (Dyn-DNS) server through the vulnerability of IoT devices, which led to the closure of many websites including the Twitter giant company [11]. As we can see, it is crucial to solve the security problems of the IoT. Nowadays, the IoT security protection still adopts the wireless network security strategy, which determines the identity of the legitimate users by the Internet Protocol (IP) address [12; 13], Media Access Control (MAC) address [14] or the key encryption scheme [15]. To be specific, In the Open System Interconnection (OSI) model [16], the third layer of the network layer is responsible for the IP address, and the second layer of the data link layer is responsible for the MAC address. The MAC address is used to uniquely identify a network card in the network. If one device has one or more network cards, each network card needs and has a unique MAC address. Because the MAC address consists of hexadecimal digits, the intruders can illegally attack IoT devices by counterfeiting the above identifications. So, we consider the features of the wireless signals as the individual identification feature of IoT devices, which can accurately identify unauthorized users who are attempting to join the network. For example, not only the DDoS attack but also the other attacks will be prevented in the physical layer. In brief, physical layer security is an effective way to enhance the security of the IoT. In general, the process of detection and recognition of the wireless signals is composed of collection, extraction, training, and classification. First of all, we need to collect wireless signals and extract the features representing the individual identification. Then, it is important to obtain the trained classification by training the classifier adaptively. Finally, the model of the classifier can be used to detect the identification of the IoT devices. As for the classification, the algorithms of machine learning include k-nearest neighbor (KNN) classifier [17], back propagation (BP) neural network classifier [18] and support vector machine (SVM) classifier [19], etc., but the above algorithms are all used for closed-set recognition, which is defined that the test sets are all known classes. Machine learning models trained based on closed-set data can only identify the samples that are the same prior probability distributions with the training sets. For unknown classes, the model only discriminates as a known class in the closed set with errors.