### Paper Contribution  
In this paper, we focus on a scenario where, first, legitimate devices are authenticated by means of a higher level procedure, and a unique ID is assigned to each of them. Successively, during communication, the sink node performs a continuous PLA (and spoofing detection), which uses multiple PHY layer attributes to verify the correspondence of the WF of each user with the assigned ID. In particular, the continuous authentication is performed by a two-step procedure:  
1. Each transmitting node is identified by means of a ML supervised non-parametric classification algorithm where training data are labeled (i.e., it is associated to the corresponding user's ID). It means that an eventual malicious node is not detected at this step, but it is classified as belonging to one of the authorized nodes' class. 2. In order to detect a spoofing attack, a successive cross-check of the PHY layer classification results and the ID declared by the transmitting node is performed. The cross check of the ID and PLA outcome gives a higher level of protection with respect to the exclusive use of an ID: the ID can be stolen, and while the PLA aims for supporting the legitimate devices by a reciprocal wireless link, the wireless channel features can be used as an additional unique security signature. The spoofing detection capability increases with the network dimension (i.e., the number of nodes), and this is important for future IoT systems where a massive machine access is foreseen. However, we propose also the introduction of _sentinel nodes_, which can significantly enhance the detection capability, especially in small networks. Summarizing the contributions of the paper are the following:  
* Proposal of a continuous authentication/spoofing detection system, suitable for an actual WSN, where multiple IoT nodes communicate with a sink node. Differently, previous works on PLA [16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28] were usually based on scenarios with a single authorized node that must be distinguished by the unauthorized one, and hence, only a binary decision is needed (i.e., binary classification). However, binary classification is not suitable for large-scale IoT networks, and extending these methods to a multi-user scenario may not be straightforward. In any case, it means seeking an optimal threshold individually for each IoT legitimate node, which is expensive in terms of resources and signaling. Moreover, adopting a ML approach means adopting and training one machine for each node. Here, instead, the malicious user must be distinguished by multiple authorized nodes using their WFs. This is a more complex scenario because there is a higher variability of legitimate channels, and the probability that the spoofing attacker is close to one of them is higher. The proposed approach allows to simultaneously distinguish the malicious node from all the legitimate ones, using a single machine. * We propose a threshold-free method, thanks to the integration of the classification of devices using their PHY-layer attributes and the associated device ID. Differently, in most of the approaches proposed in the literature, a threshold is needed to distinguish between legitimate and malicious user data [16; 17; 18; 19; 20; 21; 24; 26]. However, the threshold must be optimized for each scenario with a consequent performance degradation, especially in time-varying scenarios. * We propose a method that does not require any knowledge and specific statistical distribution, neither for the PHY-layer attributes nor for the spoofing model. This makes the proposed system more applicable in actual contexts. Conversely, many of the proposed approaches in the literature make assumptions difficult to obtain in a real environment, such as specific channel models and knowledge of data belonging to the attacker [16; 18; 22; 26; 28]. * We investigate a solution for a supervised classification of devices based on CART and random forest algorithms that were not previously investigated in this context.