Improved GNSS Localization and Byzantine Detection in UAV Swarms

Hacohen, Shlomi; Medina, Oded; Grinshpoun, Tal; Shvalb, Nir

doi:10.3390/s20247239

Cited by 9 publications

(9 citation statements)

References 57 publications

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“…Hacohen et al [76] proposed a GNSS spoofing detection mechanism in UAV swarms that leverage the IMUs and the GNSS (GPS) receivers. Additionally, an onboard sensor that estimates the relative distance between the drones (like LoRa Sensor) is employed in this scheme.…”

Section: B Gps Spoofing Defense Mechanisms In Uavsmentioning

confidence: 99%

“…In terms of Accuracy, signal processing-based methods [85], [87] also show relatively high results of 98.4% and 96.2%, respectively. The methods that rely on onboard sensors such as [66] and [76] showcased greater Accuracy of 97% and 90%, respectively. The methods that rely on signal processing and onboard sensors were efficient in terms of detection time (latency) as well, which was less than two seconds in [81].…”

Section: E Comparative Analysismentioning

confidence: 99%

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GPS Spoofing Attacks in FANETs: A Systematic Literature Review

Altaweel,

Mukkath,

Kamel

2023

IEEE Access

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Flying Ad-Hoc Networks (FANETs) are groups of UAVs connected in an Ad-Hoc manner to accomplish a common mission. The widespread acceptance of UAVs due to their low cost and high efficiency has attracted malicious security attacks against them. These attacks cannot be easily prevented due to UAVs' limited computational power, short battery life, and inability to execute complex algorithms. FANETs rely on the Global Positioning System (GPS) for localization. GPS Spoofing, an easy-to-launch attack, is one of the main challenges in FANETs. In which, the legitimate and not-encrypted civilian GPS signals are overridden by counterfeit signals to deceive the UAVs to collide or to be hijacked. Researchers proposed various countermeasures to address GPS Spoofing attacks in FANETs. To further assist future research, this paper provides a systematic literature review on GPS Spoofing attacks in FANETs and their defense mechanisms. After formulating eight research questions and applying well-defined inclusion/exclusion criteria and quality assessment tools, 70 research articles were extracted. The existing defense mechanisms were classified based on their objectives (i.e., detection, mitigation, and/or prevention) and according to their basis (i.e., relying on readings from various drones' devices/sensors, processing the signals received by various sensors, employing machine learning algorithms, relying on game theory, or leveraging cryptographic techniques to authenticate and protect the confidentiality of GPS signals). The defense mechanisms were also analyzed to identify the attacker models, impacts of the attack, and detection performance. This study found that most of the proposed methods are detection approaches, rather than mitigation or prevention. Also, almost all papers used simulation experiments rather than a proof-of-concept implementation, which does not demonstrate a convincing performance under realistic mobility and propagation models. Moreover, most solutions addressed GPS Spoofing for a single UAV. Only eight articles addressed multiple UAV scenarios and none of them provided a proof-of-concept evaluation. INDEX TERMSDrones, unmanned ariel vehicle (UAV), Flying Ad-Hoc NETwork (FANET), GPS spoofing attack, wireless network security, systematic literature review. * Spoofing tools such as simulators, repeaters, etc. * Spoofing techniques such as position denial and track break. * Spoofer location such as remote, onboard, and escort. --Defense mechanisms against GPS Spoofing: the research articles that discuss various mechanisms to defend against GPS Spoofing attacks in UAVs. These articles are further classified based on their objectives as follows: * Detection of GPS Spoofing attacks. * Mitigation the damage caused by GPS Spoofing attacks. * Prevention of GPS Spoofing. The defense mechanisms are also classified according to their basis as follows: * The use of devices/sensors on the drones. * Signal processing techniques applied on various signals received by various sensors on the drone. * Cryptographic techn...

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Section: B Gps Spoofing Defense Mechanisms In Uavsmentioning

confidence: 99%

Section: E Comparative Analysismentioning

confidence: 99%

GPS Spoofing Attacks in FANETs: A Systematic Literature Review

Altaweel,

Mukkath,

Kamel

2023

IEEE Access

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“…While Byzantine fault tolerance has received significant attention in distributed machine learning research, its application to mitigate Byzantine attacks in multi-UAV systems has been limited to only a few works [28][29][30][31][32][33]. Its practical implementation in real-world scenarios has been relatively unexplored in the literature.…”

Section: Byzantine Fault Tolerance In Multi-uav Systemsmentioning

confidence: 99%

Robust Multiagent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks

Medhi,

Liu,

Wang

et al. 2023

Information

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Multiple unmanned aerial vehicle (multi-UAV) systems have gained significant attention in applications, such as aerial surveillance and search and rescue missions. With the recent development of state-of-the-art multiagent reinforcement learning (MARL) algorithms, it is possible to train multi-UAV systems in collaborative and competitive environments. However, the inherent vulnerabilities of multiagent systems pose significant privacy and security risks when deploying general and conventional MARL algorithms. The presence of even a single Byzantine adversary within the system can severely degrade the learning performance of UAV agents. This work proposes a Byzantine-resilient MARL algorithm that leverages a combination of geometric median consensus and a robust state update model to mitigate, or even eliminate, the influence of Byzantine attacks. To validate its effectiveness and feasibility, the authors include a multi-UAV threat model, provide a guarantee of robustness, and investigate key attack parameters for multiple UAV navigation scenarios. Results from the experiments show that the average rewards during a Byzantine attack increased by up to 60% for the cooperative navigation scenario compared with conventional MARL techniques. The learning rewards generated by the baseline algorithms could not converge during training under these attacks, while the proposed method effectively converged to an optimal solution, proving its viability and correctness.

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“…GNSS traffic estimation allows for real-time, efficient, wideregion, and inexpensive use of a stand-alone device [24]. However, GNSS errors are caused by multipath and NLOS (non-line-of-sight) effects, which become severe in urban areas [25]. In particular, GNSS pseudorange positioning is not accurate enough for lanelevel bus tracking [26].…”

Section: Introductionmentioning

confidence: 99%

Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information

Yang

Yan

Guo

et al. 2021

Sensors

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The driving behavior of bus drivers is related to the safety of all passengers and regulation of urban traffic. In order to analyze the relevant characteristics of speed and acceleration, accurate bus trajectories and patterns are essential for driver behavior analysis and development of effective intelligent public transportation. Exploiting real-time vehicle tracking, this paper develops a platform with vehicle-mounted terminals using differential global navigation satellite system (DGNSS) modules for driver behavior analysis. The DGNSS traces were used to derive the vehicle trajectories, which were then linked to road information to produce speed and acceleration matrices. Comprehensive field tests were undertaken on multiple bus routes in urban environments. The spatiotemporal results indicate that the platform can automatically and accurately extract the driving behavior characteristics. Furthermore, the platform’s visual function can be used to effectively monitor driving risks, such as speeding and fierce acceleration, in multiple bus routes. The details of the platform’s features are provided for intelligent transport system (ITS) design and applications.

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Improved GNSS Localization and Byzantine Detection in UAV Swarms

Cited by 9 publications

References 57 publications

GPS Spoofing Attacks in FANETs: A Systematic Literature Review

GPS Spoofing Attacks in FANETs: A Systematic Literature Review

Robust Multiagent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks

Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information

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