Browsing by Author "Shen, Yulong"

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Deep Ensemble Learning based GPS Spoofing Detection for Cellular-Connected UAVs
(2022-12-15) Dang, Yongchao; Benzaid, Chafika; Yang, Bin; Taleb, Tarik; Shen, Yulong
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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 open-source simulator can use commercial software-defined radio tools to generate fake 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 at the same time. In this paper, 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 base stations 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 multi-layer 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.
GPS Spoofing Detector with Adaptive Trustable Residence Area for Cellular based-UAVs
(2020-12) Dang, Yongchao; Benzaid, Chafika; Shen, Yulong; Taleb, Tarik
A4 Artikkeli konferenssijulkaisussa
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%.
Incentive Jamming-Based Secure Routing in Decentralized Internet of Things
(2021-02-15) Xu, Yang; Liu, Jia; Shen, Yulong; Liu, Jun; Jiang, Xiaohong; Taleb, Tarik
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This article focuses on the secure routing problem in the decentralized Internet of Things (IoT). We consider a typical decentralized IoT scenario composed of peer legitimate devices, unauthorized devices (eavesdroppers), and selfish helper jamming devices (jammers), and propose a novel incentive jamming-based secure routing scheme. For a pair of source and destination, we first provide theoretical modeling to reveal how the transmission security performance of a given route is related to the jamming power of jammers in the IoT. Then, we design an incentive mechanism with which the source pays some rewards to stimulate the artificial jamming among selfish jammers, and also develop a two-stage Stackelberg game framework to determine the optimal source rewards and jamming power. Finally, with the help of the theoretical modeling as well as the source rewards and jamming power setting results, we formulate a shortest weighted path-finding problem to identify the optimal route for secure data delivery between the source-destination pair, which can be solved by employing the Dijkstra's or Bellman-Ford algorithm. We prove that the proposed routing scheme is individually rational, stable, distributed, and computationally efficient. Simulation and numerical results are provided to demonstrate the performance of our routing scheme.
Performance, Fairness and Tradeoff in UAV Swarm Underlaid mmWave Cellular Networks with Directional Antennas
(2021-04) Yang, Bin; Taleb, Tarik; Shen, Yulong; Jiang, Xiaohong; Yang, Weidong
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Unmanned aerial vehicle (UAV) swarm connected to millimeter wave (mmWave) cellular networks is emerging as a new promising solution to provide ubiquitous high-speed and long distance wireless communication services for supporting various applications. To satisfy different quality of service (QoS) requirements in future large-scale applications of such networks, this article investigates the rate performance, fairness and their tradeoff in the networks with directional antennas in terms of sum-rate maximization, fairness index maximization, max-min fair rate and proportional fairness. We first consider a more realistic mmWave 3D directional antenna array model for UAVs and base station (BS), where the antenna gain depends on the radiation angle of the antenna array. Based on this antenna array model, we formulate the performance, fairness and their tradeoff as four constrained optimization problems, and propose corresponding iterative algorithm to solve these problems by jointly optimizing elevation angle, azimuth angle and height of antenna array at BS in the downlink transmission scenario. Furthermore, we also explore them in uplink transmission scenario, where the interference issue among links is carefully considered. Finally, according to the sum rate, minimum rate and fairness index under each optimization problem, numerical results are provided to illustrate the impacts of network parameters on the performance, fairness and their tradeoff, and also to reveal new findings under both downlink and uplink transmission scenarios, respectively.
Transfer Learning based GPS Spoofing Detection for Cellular-Connected UAVs
(2022-07-19) Dang, Yongchao; Benzaid, Chafika; Taleb, Tarik; Yang, Bin; Shen, Yulong
A4 Artikkeli konferenssijulkaisussa
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.