Mitigating GPS spoofing in AVS: SHA and RSA algorithms with proteus simulation for real-time detection

B Poornima () and Lalitha Surya Kumari ()
Additional contact information
B Poornima: Koneru Lakshmaiah Education Foundation, Mahatma Gandhi Institute of Technology
Lalitha Surya Kumari: Koneru Lakshmaiah Education Foundation

International Journal of System Assurance Engineering and Management, 2025, vol. 16, issue 10, No 11, 3375-3389

Abstract: Abstract Smart car Autonomous Vehicular Systems (AVS) face significant threats from GPS spoofing, which can dangerously manipulate a vehicle’s perceived location. This paper presents a robust and enhanced mitigation framework that combines machine learning-based detection, cryptographic safeguards, and real-time alert systems. The proposed methodology begins with system initialization and GPS signal reception, followed by a data consistency check that flags suspicious signals. Statistical analysis and supervised machine learning, developed in prior work, identify spoofed signals based on key features such as PDOP, HDOP, and VDOP, with PDOP emerging as a primary indicator. The Extra Trees Regressor model demonstrates superior performance in handling these complex interactions, with visualizations such as heatmaps and scatter plots offering deeper insights. To further enhance reliability, an MPU6050 accelerometer-gyroscope module is integrated to continuously monitor real-time motion and orientation. This enables inertial measurements to cross-verify GPS data, especially in spoofing scenarios or environments with signal obstructions. When spoofing is confirmed, the system triggers LED indicators and sends alerts via GSM, while normal data is hashed using SHA-256, encrypted with RSA, and securely transmitted. This comprehensive approach ensures high integrity, real-time spoofing detection, and system resilience, significantly enhancing safety and operational reliability in AVS and other GPS-dependent applications.

Keywords: GPS spoofing detection; Autonomous vehicular systems (AVS); Machine learning; Cryptographic security; Inertial measurement (MPU6050) (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-025-02864-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:16:y:2025:i:10:d:10.1007_s13198-025-02864-8

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-025-02864-8

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().