GPS Spoofing Detection Method for Small UAVs Using 1D Convolution Neural Network

Sung, Young-Hwa; Park, Soo-Jae; Kim, Dong Yeon; Kim, Sungho

doi:10.3390/s22239412

Cited by 17 publications

(6 citation statements)

References 28 publications

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“…In addition to the aforementioned deep learning techniques, spoofing attack detection techniques based on CNN have also been used. Using a simple model, the study [39] a novel antispoofing technique. The suggested solution used the ResNet architecture, which made it more effective than SVM at detecting most spoof signals.…”

Section: Gps Spoofing Attacks Classification Using Deep Learning Modelsmentioning

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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Section: Gps Spoofing Attacks Classification Using Deep Learning Modelsmentioning

confidence: 99%

“…Scalability concerns for varying attack types; generalizability not thoroughly addressed. [39] 2022 Real-time dataset 1D convolutional neural network…”

Section: Pca-cnn-lstmmentioning

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

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show abstract

“…GPS spoofing attacks can lead AVs to lose control and potentially result in accidents or unauthorized access to sensitive areas. The following diagram demonstrates how a GPS spoofing attack works [11].…”

Section: Blockchain-based Design To Reduce Data Spoofingmentioning

confidence: 99%

“…While blockchain holds promise for enhancing the security of autonomous vehicle systems, it is essential to address challenges to fully utilize the potential efficiency of the network [38]. Overcoming these issues will require collaboration between the stakeholders, testing new designs on the live data so that the regulatory flaws can be identified, and ongoing research efforts for the seamless integration of blockchain technology into the future of autonomous transportation [11].…”

Section: Limited Existing Systemsmentioning

confidence: 99%

How can Blockchain be Integrated into Autonomous Systems to Ensure Data Integrity and Trustworthiness, and What are the Potential Pitfalls in Decentralized Autonomous System Operations?

Mesci,

Eren

2024

AJRCoS

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Real-time applications always have security issues present in their ecosystem which needs to be improved. Blockchain technology can be considered as a solution that can ensure network security. The integration of blockchain technology into autonomous systems has now become an important candidate to resolve network security issues in devices that work on p2p communications. As humans become more reliant on autonomous vehicles, the importance of finding a solution to the problems linked with data security needs to be addressed to comply with international security laws. The paper aims to find the importance of blockchain technology and how it can be integrated into different classes of autonomous vehicles. In the research, an analysis of existing papers will be included along with a proposed design that can be deployed to solve the problems associated with data security using blockchain technology. Besides the improvements offered by blockchain technology, all the additional challenges associated with designing a new system and deploying this technology will be reviewed. The limitations of the works along with the future research opportunities that might open up will be presented in this paper.

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“…In addition, deep learning-based convolutional neural networks showed promising performance in medical detection, GPS, and risk assessment [ 30 , 31 , 32 ]. Recent research on binary neural networks (BNNs) has also been successively published.…”

Section: Related Workmentioning

confidence: 99%

An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression

Luo

Xiong

et al. 2023

Sensors

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Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.

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GPS Spoofing Detection Method for Small UAVs Using 1D Convolution Neural Network

Cited by 17 publications

References 28 publications

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

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

How can Blockchain be Integrated into Autonomous Systems to Ensure Data Integrity and Trustworthiness, and What are the Potential Pitfalls in Decentralized Autonomous System Operations?

An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression

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