BD-II NMA&amp;SSI: An Scheme of Anti-Spoofing and Open BeiDou II D2 Navigation Message Authentication

et al. 2020

IEEE Access

Self Cite

A large number of facts prove that Global Navigation Satellite System (GNSS) has certain security risks and is threatened by attacks. In particular, GNSS civilian signal receivers have some vulnerability in responding to spoofing attack and jamming attack, because the format and modulation of GNSS civilian signals are public. Based on the concerns of existing GNSS spoofing scenarios, this paper reviews the research on GNSS anti-spoofing technologies from two aspects. In this paper, the strategies of spoofing attacks discovered at this stage are classified in detail, the protection effect and scheme implementation complexity of various anti-spoofing techniques against spoofing attacks are analyzed from the perspective of signal-level and data-level. More specifically, we comprehensively analyze the ability for the combined anti-spoofing methods at the signal-level and the data-level to protect from combinations of different spoofing attack. Finally, the future development trend and potential research direction of GNSS anti-spoofing technology are summarized and predicted.

Section: Discussionmentioning

confidence: 99%

Section: A Navigation Message Authenticationmentioning

confidence: 99%

Spoofing and Anti-Spoofing Technologies of Global Navigation Satellite System: A Survey

Zhang

et al. 2020

IEEE Access

Self Cite

“…For instance, Wesson et al [18] combined signature-based authentication of GPS navigation messages with a statistical hypothesis test to prevent counterfeit navigation messages. Wu et al [14] leveraged SM cryptographic algorithms to authenticate the BeiDou-II navigation messages. Similarly, the work in [19] presented a BeiDou-II navigation message authentication scheme based on digital signatures generated by an elliptic curve digital signature algorithm.…”

Section: B Cryptal-protected Navigation Message Approachesmentioning

confidence: 99%

Deep-Ensemble-Learning-Based GPS Spoofing Detection for Cellular-Connected UAVs

Dang

Benzaïd

IEEE Internet Things J.

et al. 2022

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.

“…The GPS Navigation Message Authentication (NMA) approach protects the civil GPS signal from attacking by embedding the cryptographic signature into the navigation messages. Wu et al presented a BeiDou-II NMA scheme based on digital signatures generated by Elliptic Curve Digital Signature Algorithm (ECDSA) in [9] and leveraged SM cryptographic algorithms to authenticate the BeiDou-II navigation messages in [10]. Wesson et al [11] prevented counterfeit navigation messages by combining signature-based security methods with hypothesis tests.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for GPS Spoofing Detection in Cellular-Enabled UAV Systems

Dang

Benzaïd

2021 International Conference on Networking and Network Applications (NaNA)

et al. 2021

Cellular-based Unmanned Aerial Vehicle (UAV) systems are a promising paradigm to provide reliable and fast Beyond Visual Line of Sight (BVLoS) communication services for UAV operations. However, such systems are facing a serious GPS spoofing threat for UAV's position. To enable safe and secure UAV navigation BVLoS, this paper proposes a cellular network assisted UAV position monitoring and anti-GPS spoofing system, where deep learning approach is used to live detect spoofed GPS positions. Specifically, the proposed system introduces a MultiLayer Perceptron (MLP) model which is trained on the statistical properties of path loss measurements collected from nearby base stations to decide the authenticity of the GPS position.Experiment results indicate the accuracy rate of detecting GPS spoofing under our proposed approach is more than 93% with three base stations and it can also reach 80% with only one base station.