A GPS-Adaptive Spoofing Detection Method for the Small UAV Cluster

Meng, Lianxiao; Zhang, Long; Yang, Lin; Wu, Yang

doi:10.3390/drones7070461

Cited by 3 publications

(1 citation statement)

References 36 publications

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“…Besides mechanical failures, UAVs can be subjected to software bugs and glitches, leading to erroneous behaviors that affect their stability and performance [32]. For example, navigation errors are the result of inaccurate Global Positioning System (GPS) data [33] or misprocessed data in the UAV's navigation algorithm [34], leading to deviations from the intended flight's path. Regulation and compliance standards require a continuous update of UAV's software to ensure continued adherence to legal and safety requirements [35], constituting a challenging chore.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Osmani,

Schulz

2024

Sensors

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Analysis of Attack Intensity on Autonomous Mobile Robots

Basan,

Mushenko

et al. 2024

Robotics

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Autonomous mobile robots (AMRs) combine a remarkable combination of mobility, adaptability, and an innate capacity for obstacle avoidance. They are exceptionally well-suited for a wide range of applications but usually operate in uncontrolled, non-deterministic environments, so the analysis and classification of security events are very important for their safe operation. In this regard, we considered the influence of different types of attacks on AMR navigation systems to subdivide them into classes and unified the effect of attacks on the system through their level of consequences and impact. Then, we built a model of an attack on a system, taking into account five methods of attack implementation and identified the unified response thresholds valid for any type of parameter, which allows for creating universal correlation rules and simplifies this process, as the trigger threshold is related to the degree of impact that the attack has on the finite subsystem. Also, we developed a methodology for classifying incidents and identifying key components of the system based on ontological models, which makes it possible to predict risks and select the optimal system configuration. The obtained results are important in the context of separating different types of destructive effects based on attack classes. Our study showed that it is sometimes difficult to divide spoofing attacks into classes by assessing only one parameter since the attacker can use a complex attack scenario, mixing the stages of the scenarios. We then showed how adding an attack intensity factor can make classification more flexible. The connections between subsystems and parameters, as well as the attack impact patterns, were determined. Finally, a set of unique rules was developed to classify destructive effects with uniform response thresholds for each parameter. In this case, we can increase the number of parameters as well as the type of parameter value.

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Future Trends and Emerging Threats in Drone Cybersecurity

Sindiramutty,

Jhanjhi,

Tan

et al. 2024

Advances in Information Security, Privacy, and Ethics

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The rapid proliferation of drone technology has ushered in a new era of innovation and convenience across various industries. However, this technological advancement has also given rise to a host of cybersecurity challenges that demand proactive attention. This chapter delves into the dynamic landscape of drone security, exploring the evolving trends and emerging threats that shape this field. Beginning with an overview of drone technology advancements, including the integration of artificial intelligence and machine learning, this chapter examines their implications for cybersecurity. It then scrutinizes the growing influence of drone swarms and autonomous operations, pinpointing the associated cybersecurity challenges and strategies for securing these systems. The chapter explores the expansion of drone operations beyond visual line of sight (BVLOS) and the cybersecurity risks involved, emphasizing the importance of secure communications and legal considerations.

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A GPS-Adaptive Spoofing Detection Method for the Small UAV Cluster

Cited by 3 publications

References 36 publications

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems

Analysis of Attack Intensity on Autonomous Mobile Robots

Future Trends and Emerging Threats in Drone Cybersecurity

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