Document Type
Thesis - Open Access
Award Date
2024
Degree Name
Master of Science (MS)
Department / School
Electrical Engineering and Computer Science
First Advisor
Kwanghee Won
Second Advisor
Young Chang
Abstract
As autonomous vehicles (AVs) become integral to modern transportation, their susceptibility to cyber-attacks, particularly GPS spoofing, presents a serious security threat. This study addresses these challenges by applying a suite of deep learning models to enhance the detection of anomalous GPS signals. Focusing on autoencoder-based architectures, the proposed models such as long short-term memory-based variational autoencoder (LSTM-VAE), LSTM-based autoencoder (LSTM-AE), multilayer perceptron-based variational autoencoder (MLP-VAE), MLP-based Autoencoder (MLPAE), Stacked LSTM-based variational autoencoder (Stacked-LSTM-VAE), stacked LSTM-based autoencoder (Stacked-LSTM-AE), memory-augmented-LSTM-VAE (Mem-LSTM-VAE), and time-series-anomaly-detection-generative-adversarial-networks (TadGAN) were trained exclusively on authentic GPS data. This unsupervised learning approach which used for the above-mentioned models enables the models to accurately identify deviations from normal patterns, thereby enhancing their ability to detect spoofed signals through detailed reconstruction error analysis. In this thesis a comparative analysis highlights the differing strengths of these models in distinguishing between authentic and spoofed signals, with notable performance exhibited by the architectures of MLP-AE and Stacked-LSTM. The MLP-AE model achieved a detection accuracy of 95.40%, precision of 93.09%, and a ROC-AUC score of 94.35%. Similarly, the deeper learning structure of the Stacked-LSTM models demonstrated high efficacy in handling complex and noisy data, critical for high-stakes applications such as AV navigation. These results emphasize the potential of combining autoencoder-based models with MLP and Stacked-LSTM architectures to strengthen AV security against GPS spoofing attacks. By demonstrating the effectiveness of unsupervised learning in anomaly detection, this study presents a robust framework for improving the security of GPS-dependent technologies, laying the foundation for future advancements in safeguarding AV systems against evolving cyber threats.
Library of Congress Subject Headings
Anomaly detection (Computer security)
Deep learning (Machine learning)
Global Positioning System -- Security measures.
Machine learning -- Security measures.
Automated vehicles.
Publisher
South Dakota State University
Recommended Citation
Mirzakhaninafchi, Hasan, "Comparative Analysis of Deep Learning-Based Anomaly Detection Models for GPS Spoofing Detection" (2024). Electronic Theses and Dissertations. 1351.
https://openprairie.sdstate.edu/etd2/1351
Rights Statement
