Ambient backscatter communication (AmBC) has become an integral part of ubiquitous Internet of Things (IoT) applications due to its energy-harvesting capabilities and ultra-low- power consumption. However, the open wireless environment exposes AmBC systems to various attacks, and existing authentication methods cannot be implemented between resource-constrained backscatter devices (BDs) due to their high computational demands. To this end, this paper proposes PLCRA-BD, a novel physical layer challenge-response authentication scheme between BDs in AmBC that overcomes BDs’ limitations, supports high mobility, and performs robustly against impersonation and wireless attacks. It constructs embedded keys as physical layer fingerprints for lightweight identification and designs a joint transceiver that integrates BDs’ backscatter waveform with receiver functionality to mitigate interference from ambient RF signals by exploiting repeated patterns in orthogonal frequency division multiplexing (OFDM) symbols. Based on this, a challenge-response authentication procedure is introduced to enable low-complexity fingerprint exchange between two paired BDs leveraging channel coherence, while securing the exchange process using a random number and unpredictable channel fading. Additionally, we optimize the authentication procedure for high-mobility scenarios, completing exchanges within the channel coherence time to minimize the impact of dynamic channel fluctuations. Security analysis confirms its resistance against impersonation, eavesdropping, replay, and counterfeiting attacks. Extensive simulations validate its effectiveness in resource-constrained BDs, demonstrating high authentication accuracy across diverse channel conditions, robustness against multiple wireless attacks, and superior efficiency compared to traditional authentication schemes.

Ambient backscatter communication (AmBC) has become increasingly popular for facilitating ubiquitous Internet of Things (IoT) due to its ultra-low-power consumption and energy-harvesting capabilities. However, the open nature of wireless channels and the resource-limited devices in AmBC systems expose them to threats such as identity impersonation and wireless spoofing attacks. Recently, physical layer authentication (PLA) offered a lightweight solution to address the above security challenges by utilizing inherent wireless properties, like channel fading and signal patterns, as unique fingerprints to identify legitimate devices and distinguish them from attackers. Despite its promise, current research still lacks an effective PLA mechanism that can accurately authenticate backscatter devices (BDs) and defend against spoofing attacks in AmBC systems. This paper presents AmbAu, a two-stage PLA scheme designed to authenticate BDs by leveraging the signal correlation between an ambient source and the BD. In the initialization stage, the verifier extracts a complex covariance matrix from the downlink signals between the ambient source and the BD. This matrix is later used during the authentication stage to verify the BD's identity and detect spoofing attempts. Through security analysis, we demonstrate AmbAu's resilience against replay, relay, and counterfeiting attacks. Simulations under various conditions demonstrate AmbAu's accuracy, efficiency, and superior performance when compared to traditional PLA schemes.

The rapid growth of smartphones, laptops, and IoT devices has created an increasing demand for seamless, secure, and energy-efficient wireless direct communication. Current popular solutions, such as AirDrop and Intel Unison, rely on Bluetooth Low Energy (BLE) for device discovery and pairing, alongside IEEE 802.11 (Wi-Fi) for high-speed data transmission. However, research has identified significant vulnerabilities in BLE, including information leakage and profile manipulation. To address these challenges, we propose an Enhanced Wireless Direct Connectivity (EWDC) framework that integrates Digital Enhanced Cordless Telecommunications (DECT)-2020 New Radio (NR) with Wi-Fi. In this framework, DECT replaces BLE to enable secure and efficient device discovery, while Wi-Fi supports high-bandwidth data transmission. Specifically, DECT utilizes its licensed spectrum to establish a secure communication channel for sharing Wi-Fi configuration information, enabling a secure and reliable setup of high-speed Wi-Fi data transmission links between devices. To validate this approach, we implemented the DECT MAC layer on the Nordic nRF9161-DK platform and evaluated the end-to-end delay between two devices. Our results demonstrate an impressive transmission delay of less than 1 μs for EWDC communication, enabling rapid device discovery and highly efficient operation. Furthermore, to enhance the security of wireless direct connections, the WireGuard, a VPN tunnel, is employed to encrypt data transmission over Wi-Fi providing robust protection for data shared on public frequency bandwidths. The proposed hybrid DECT-2020 NR and Wi-Fi solution significantly improves reliability, security, and communication efficiency, positioning it as a robust candidate for next-generation IoT communication systems.

The cellular-connected Unmanned Aerial Vehicles (UAVs) are emerging as integral components of the 5G and beyond system due to their mobility and flexibility. Compared to a traditional single UAV, a flock of UAVs established as a UAV swarm can implement diverse distributed applications economically and efficiently, such as cooperatively smart agriculture, joint search and rescue, and supplementing temporary network connections. However, the GPS spoofing attack can manipulate UAV swarm locations and distort UAV swarm topology, which threatens the security of swarm communication and control. This paper proposes a Graphic Neural Networks (GNN) based GPS spoofing detection approach for cellular-connected UAV swarms. Especially, we propose a system in which the GNN model is used to detect GPS spoofing attacks by analyzing the similarity between the swarm GPS topology and communications topology. To evaluate the proposed neural networks, we use a bipartite graph and Hungarian algorithm to build a UAV swarm simulator. The results show that GNN can efficiently compute topologies’ similarity and detect GPS spoofing attacks. For instance, for a UAV swarm consisting of 10 UAVs, GNN detects the spoofing with accuracy over 90% and computation time of fewer than 10 milliseconds using Intel Core 1.6 GHz processor.

Unmanned Aerial Vehicles (UAVs) are set to become an integral part of 5G and beyond systems with the promise of assisting cellular communications and enabling advanced applications and services, such as public safety, caching, and virtual/mixed reality-based remote inspection. However, safe and secure navigation of UAVs is a key requisite for their integration in the airspace. The GPS spoofing is one of the major security threats to remotely and autonomously controlled UAVs. In this paper, we propose a machine learning-based, mobile network-assisted UAV monitoring and control system that allows live monitoring of UAVs' locations and intelligent detection of spoofed positions. We introduce the Convolutional Neural Network (CNN) in the edge UAV Flight Controller (UFC) to locate a UAV and detect any GPS spoofing by comparing differences between the theoretical path loss computed by UFC and the corresponding path loss reported by the connected base station (BS). To reduce the detection latency as well as to increase the detection accuracy, transfer learning is leveraged to transfer the CNN knowledge between edge servers when the UAV handovers from one BS to another. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with an accuracy rate above 88% using only one BS.

Unmanned aerial vehicles (UAVs) are an emerging technology in the 5G-and-beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the opensource simulator can use commercial software-defined radio tools to generate fake global positioning system (GPS) signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations (BSs) at the same time. In this article, we propose a novel deep-ensemble-learning-based, mobile-network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between BSs and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multilayer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.

The envisioned key role of Unmanned Aerial Vehicles (UAVs) in assisting the upcoming mobile networks calls for addressing the challenge of their secure and safe integration in the airspace. The GPS spoofing is a prominent security threat of UAVs. In this paper, we propose a 5G-assisted UAV position monitoring and anti-GPS spoofing system that allows live detection of GPS spoofing by leveraging Uplink received signal strength (RSS) measurements to cross-check the position validity. We introduce the Adaptive Trustable Residence Area (ATRA); a novel strategy to determine the trust area within which the UAV's GPS position should be located in order to be considered as non-spoofed. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with a rate of above 95%.