Real-Time jamming detection using windowing and hybrid machine learning models for pre-saturation alerts
Abstract This paper proposes a new deep learning and machine learning model for detecting deception and suppression jamming in Ublox-M8T receivers operating under GNSS interference. This solution employs XGBoost for real-time classification of jamming signals, implemented on an STM32H743 microcontro...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10567-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849343675872575488 |
|---|---|
| author | J. Sormayli M. Darvishi K. Zarrinnegar M. R. Mosavi |
| author_facet | J. Sormayli M. Darvishi K. Zarrinnegar M. R. Mosavi |
| author_sort | J. Sormayli |
| collection | DOAJ |
| description | Abstract This paper proposes a new deep learning and machine learning model for detecting deception and suppression jamming in Ublox-M8T receivers operating under GNSS interference. This solution employs XGBoost for real-time classification of jamming signals, implemented on an STM32H743 microcontroller to ensure ultra-low latency, making it suitable for navigation in various environments. This work’s key contribution is integrating a windowing mechanism for pre-saturation alerts and early activation of jamming detection which enhances system reliability by distinguishing between high-credibility and low-credibility GNSS data under static and dynamic jamming conditions. To validate the model, a series of experiments were conducted using a software-defined radio transmitter to simulate jamming scenarios. Genuine GNSS and jamming signals were collected under controlled conditions, and the data were pre-processed through feature normalization, correlation analysis, and feature selection based on importance in the mentioned systems. The XGBoost classifier, trained and tested on this processed dataset, achieved a detection rate of 99.97%, a precision of 99.94%, and a Matthews correlation coefficient of 0.9992, with an average prediction time of only 20 microseconds per sample in the implemented mode, making it an excellent choice for real-time systems. Additionally, the windowing mechanism enhances system performance by proactively initiating countermeasures before reaching saturation, ensuring continuous operation during high-intensity jamming attacks. |
| format | Article |
| id | doaj-art-d25c22fbd1e441a59b38bf90a99ce886 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d25c22fbd1e441a59b38bf90a99ce8862025-08-20T03:42:53ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-10567-0Real-Time jamming detection using windowing and hybrid machine learning models for pre-saturation alertsJ. Sormayli0M. Darvishi1K. Zarrinnegar2M. R. Mosavi3Department of Electrical Engineering, Iran University of Science and TechnologyDepartment of Electrical Engineering, Iran University of Science and TechnologyDepartment of Electrical Engineering, Iran University of Science and TechnologyDepartment of Electrical Engineering, Iran University of Science and TechnologyAbstract This paper proposes a new deep learning and machine learning model for detecting deception and suppression jamming in Ublox-M8T receivers operating under GNSS interference. This solution employs XGBoost for real-time classification of jamming signals, implemented on an STM32H743 microcontroller to ensure ultra-low latency, making it suitable for navigation in various environments. This work’s key contribution is integrating a windowing mechanism for pre-saturation alerts and early activation of jamming detection which enhances system reliability by distinguishing between high-credibility and low-credibility GNSS data under static and dynamic jamming conditions. To validate the model, a series of experiments were conducted using a software-defined radio transmitter to simulate jamming scenarios. Genuine GNSS and jamming signals were collected under controlled conditions, and the data were pre-processed through feature normalization, correlation analysis, and feature selection based on importance in the mentioned systems. The XGBoost classifier, trained and tested on this processed dataset, achieved a detection rate of 99.97%, a precision of 99.94%, and a Matthews correlation coefficient of 0.9992, with an average prediction time of only 20 microseconds per sample in the implemented mode, making it an excellent choice for real-time systems. Additionally, the windowing mechanism enhances system performance by proactively initiating countermeasures before reaching saturation, ensuring continuous operation during high-intensity jamming attacks.https://doi.org/10.1038/s41598-025-10567-0Jamming interferenceMachine learningDeep learningWindowingHybrid modelsReal-Time detection |
| spellingShingle | J. Sormayli M. Darvishi K. Zarrinnegar M. R. Mosavi Real-Time jamming detection using windowing and hybrid machine learning models for pre-saturation alerts Scientific Reports Jamming interference Machine learning Deep learning Windowing Hybrid models Real-Time detection |
| title | Real-Time jamming detection using windowing and hybrid machine learning models for pre-saturation alerts |
| title_full | Real-Time jamming detection using windowing and hybrid machine learning models for pre-saturation alerts |
| title_fullStr | Real-Time jamming detection using windowing and hybrid machine learning models for pre-saturation alerts |
| title_full_unstemmed | Real-Time jamming detection using windowing and hybrid machine learning models for pre-saturation alerts |
| title_short | Real-Time jamming detection using windowing and hybrid machine learning models for pre-saturation alerts |
| title_sort | real time jamming detection using windowing and hybrid machine learning models for pre saturation alerts |
| topic | Jamming interference Machine learning Deep learning Windowing Hybrid models Real-Time detection |
| url | https://doi.org/10.1038/s41598-025-10567-0 |
| work_keys_str_mv | AT jsormayli realtimejammingdetectionusingwindowingandhybridmachinelearningmodelsforpresaturationalerts AT mdarvishi realtimejammingdetectionusingwindowingandhybridmachinelearningmodelsforpresaturationalerts AT kzarrinnegar realtimejammingdetectionusingwindowingandhybridmachinelearningmodelsforpresaturationalerts AT mrmosavi realtimejammingdetectionusingwindowingandhybridmachinelearningmodelsforpresaturationalerts |