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Delnavaz, A., Shafiee, E., Khorasani, Y. (2025). An Approach in Machine Learning for Detecting And Identifying The GNSS Spoofing Attack Based On OPTICS And GMM Algorithms. Electronic and Cyber Defense, (), -.
Amin Mehdi Delnavaz; Ebrahim Shafiee; Yaghoob Khorasani. "An Approach in Machine Learning for Detecting And Identifying The GNSS Spoofing Attack Based On OPTICS And GMM Algorithms". Electronic and Cyber Defense, , , 2025, -.
Delnavaz, A., Shafiee, E., Khorasani, Y. (2025). 'An Approach in Machine Learning for Detecting And Identifying The GNSS Spoofing Attack Based On OPTICS And GMM Algorithms', Electronic and Cyber Defense, (), pp. -.
Delnavaz, A., Shafiee, E., Khorasani, Y. An Approach in Machine Learning for Detecting And Identifying The GNSS Spoofing Attack Based On OPTICS And GMM Algorithms. Electronic and Cyber Defense, 2025; (): -.

An Approach in Machine Learning for Detecting And Identifying The GNSS Spoofing Attack Based On OPTICS And GMM Algorithms

پدافند الکترونیکی و سایبری
Articles in Press, Accepted Manuscript, Available Online from 11 September 2025
Document Type: Original Article
Authors
Amin Mehdi Delnavaz1; Ebrahim Shafiee* 2; Yaghoob Khorasani2
1Bachelor's student, Shahid Sattari University of Aeronautical Sciences and Technology, Tehran, Iran
2Assistant Professor, Shahid Sattari University of Aviation Sciences and Technology, Tehran, Iran
Receive Date: 14 April 2025,  Revise Date: 06 June 2025,  Accept Date: 05 July 2025 
Abstract
In the past decade, the protection of GNSS satellite systems against spoofing and jamming attacks has become an important focus. In a spoofing attack, the GNSS signal receiver makes errors in determining location and time because the spoofed signal is tracked by the receiver due to its close resemblance to the authentic signal and its higher power. Currently, there is no comprehensive or universal method for detecting all types of spoofing attacks. In this paper, a novel approach for detecting spoofing signals using unsupervised machine learning algorithms is presented. Two machine learning algorithms, including the density-based clustering algorithm OPTICS and the Gaussian Mixture Model (GMM), are proposed to detect spoofing attacks. These algorithms are used to distinguish between genuine GPS signals and spoofed or fake signals. The original and spoofed signals have differences in features such as phase variance, correlation distribution and signal energy, which form the basis for clustering. The performance of these algorithms has been evaluated using Silhouette scores and the confusion matrix. In addition, the algorithms were implemented and tested on a GPS software receiver. Spoofing attacks were successfully detected with an accuracy of 92.45% and 99.88% respectively.
Keywords
Machine learning; Clustering algorithm; Spoofing attack; GNSS in navigation system; OPTICS; Gaussian Mixture Model; GPS software receiver
Main Subjects
Jamming and deception(communication, radar, control, care and navigation)
References
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