ArticleSensors (Basel, Switzerland)2022
Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Early Detection of Spoofing Threats and Network Resilience Prediction in Drones Based on GRU and LSTM.Sensors (Basel, Switzerland) · 2026Article
- Multi-Layer AI Sensor System for Real-Time GPS Spoofing Detection and Encrypted UAS Control.Sensors (Basel, Switzerland) · 2026Article
- Security of ADS-B and Remote ID Systems: Cyberattacks, Detection Techniques, and Countermeasures.Sensors (Basel, Switzerland) · 2026Review
- A Physics-Informed Residual and Particle Swarm Optimization Framework for Physics-Informed UAV GPS Spoofing Detection.Sensors (Basel, Switzerland) · 2025Article
- Adversarial Evasion Attacks on SVM-Based GPS Spoofing Detection Systems.Sensors (Basel, Switzerland) · 2025Article
- Ensemble Machine Learning Models Utilizing a Hybrid Recursive Feature Elimination (RFE) Technique for Detecting GPS Spoofing Attacks Against Unmanned Aerial Vehicles.Sensors (Basel, Switzerland) · 2025Article
- CTDNN-Spoof: compact tiny deep learning architecture for detection and multi-label classification of GPS spoofing attacks in small UAVs.Scientific reports · 2025Article
- DeepSpoofNet: a framework for securing UAVs against GPS spoofing attacks.PeerJ. Computer science · 2025Article
- Detection of GPS Spoofing Attacks in UAVs Based on Adversarial Machine Learning Model.Sensors (Basel, Switzerland) · 2024Article
- Detecting and Mitigating Attacks on GPS Devices.Sensors (Basel, Switzerland) · 2024Review
- IoT empowered smart cybersecurity framework for intrusion detection in internet of drones.Scientific reports · 2023Article
- A Machine Learning Approach for Walking Classification in Elderly People with Gait Disorders.Sensors (Basel, Switzerland) · 2023Article
- GPS Spoofing Detection Method for Small UAVs Using 1D Convolution Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Editorial: Special Issue "Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking".Sensors (Basel, Switzerland) · 2022Article
- Smart Cybersecurity Framework for IoT-Empowered Drones: Machine Learning Perspective.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.