###### Abstract  
In the context of the Internet of Things (IoT), location-based applications have introduced new challenges in terms of location spoofing. With an open and shared wireless medium, a malicious spoofer can impersonate active devices, gain access to the wireless channel, as well as emit or inject signals to mislead IoT nodes and compromise the detection of their location. To address the threat posed by malicious location spoofing attacks, we develop a neural network-based model with single access point (AP) detection capability. In this study, we propose a method for spoofing signal detection and localization by leveraging a feature extraction technique based on a single AP. A neural network model is used to detect the presence of a spoofed unmanned aerial vehicle (UAV) and estimate its time of arrival (ToA). We also introduce a centralized approach to data collection and localization. To evaluate the effectiveness of detection and ToA prediction, multi-layer perceptron (MLP) and long short-term memory (LSTM) neural network models are compared. spoofing attack; UAV; IoT; neural network; localization; physical security +
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GPS is recognized as one of the most widely utilized techniques, providing high-precision coordinates. IoT systems also utilize the ToA, difference time of arrival (DToA), and received signal strength (RSS) to estimate the locations of devices [7; 8; 9]. While these techniques contribute to improving the accuracy and reliability of IoT node detection and positioning, it is essential to acknowledge their vulnerability to location spoofing attacks and signal spoofing attacks. Physical layer spoofing attacks can be categorized into passive and active attacks. First, in a passive attack, the attacker receives or listens to the legitimate signal. Eavesdropping is an example of such an attack. The eavesdropper does not propagate or transmit a signal to the target node, making it hard to detect. Second, in an active attack, the attacker intends to transmit unwanted signals to the target, injecting false signals into legitimate signals with the aim of disrupting the communication channel or misleading the IoT device to detect or locate its location. Jamming or signal spoofing attacks are examples of such attacks [10]. This paper primarily focuses on active attacks, specifically addressing one of the most critical IoT location spoofing attacks. In this context, we introduce a novel dataset based on the received power levels of both desired and undesired signals (spoofing signals). The RSS obtained from APs is directly communicated to the target node, whereby it requires location estimation. The research approach involves the development of a neural network model that utilizes this derived dataset to detect spoofed UAVs based on the received signals. Furthermore, we outline a method for locating the spoofer by collaborating with APs, as well as by estimating distances based on the predicted spoofer's ToA. This research contributes to the advancement of spoofing signal detection and localization by introducing a feature extraction method based on the received power from a single AP. First, we propose a novel approach, where a single node AP is capable of extracting signal features, which are then fed into a neural network model. This innovative technique allows for efficient spoofing signal detection using a single AP. Additionally, the proposed neural network model has the ability to estimate the spoofer's ToA based on the received power. This estimated ToA can then be used to estimate the distances from different points, providing valuable information for improving existing localization techniques. By incorporating this estimated distance information, the localization accuracy of current methods can be significantly enhanced. Furthermore, in order to accurately localize the spoofer or detect its location, it is necessary to collect data from multiple APs.