Case Study on the User Interface of GPS Plotters to Enhance Their Usability

Kim, Jae-Won; Lee, Seojeong; Jung, Min Kyo

doi:10.3390/jmse9010057

Cited by 2 publications

(4 citation statements)

References 19 publications

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“…F 1 Score = 2 × P recision × Recall P recision + Recall (10) where TP, TN, FP, and FN represent true positive, true negative, false positive, and false negative, respectively.…”

Section: A Implementation Detailsmentioning

confidence: 99%

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Enhanced Machine Learning Ensemble Approach for Securing Small Unmanned Aerial Vehicles From GPS Spoofing Attacks

Eshmawi,

Umer,

Ashraf

et al. 2024

IEEE Access

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Unmanned aerial vehicles (UAVs) substantially rely on the utilization of global positioning systems (GPS) to navigate. A simulator for commercial GPS applications with false GPS signals can lead to the deviation of a GPS-guided drone from its planned path. As a result, an anti-spoofing technology is required to assure UAV operating safety. Several approaches have been developed to detect GPS spoofing, however, predominantly such methods rely on additional hardware. Using additional hardware might not be an ideal solution for small and low-capacity UAVs. Detecting signal spoofing attacks in small UAVS has significant importance. This study presents a stacked ensemble approach to detect GPS signal spoofing within the context of small UAVs. The initial phase involves outlining the sequential procedures for obtaining and preparing the GPS signal dataset, including details about the UAV hardware, blocker, data collection timing, environmental factors, and the utilization of z-score normalization for preprocessing. Then controlled simulation tests with varying experimental conditions are conducted and the model is built using a support vector machine and convolutional neural network. Additionally, a comprehensive comparative assessment is conducted to analyze the efficacy of the proposed model against traditional machine learning models. Experimental results demonstrate notably good performance by the proposed model with a 99.74% accuracy, showing its superior performance in the context of GPS signal spoofing in small UAVs.

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“…F 1 Score = 2 × P recision × Recall P recision + Recall (10) where TP, TN, FP, and FN represent true positive, true negative, false positive, and false negative, respectively.…”

Section: A Implementation Detailsmentioning

confidence: 99%

“…Researchers have demonstrated the potential to control autonomous vehicles' paths, even causing them to deviate off-road using tools like HackRF [9]. Besides navigation, numerous applications and services also heavily rely on GPS data to enhance their functionalities and user interfaces [10].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Machine Learning Ensemble Approach for Securing Small Unmanned Aerial Vehicles From GPS Spoofing Attacks

Eshmawi,

Umer,

Ashraf

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Kim et al [1] performed a case study to identify the user requirements for standardizing the UI of GPS plotters in South Korea following the IMO guidelines to standardize the user interface (UI) of navigation equipment, such as radio detection and ranging (RADAR) and the electronic chart display and information system (ECDIS), which mandate the unification of various terminologies and icons to improve usability. Additionally, they performed a survey to ascertain how frequently the navigational functions in a GPS plotter are used.…”

Section: Papers Detailsmentioning

confidence: 99%

“…Funding: (1) This article is a result of a study on the "Leaders in Industry-University Cooperation (LINC) +" project supported by the Ministry of Education and National Research Foundation of Korea. (8)…”

mentioning

confidence: 99%