Detection and Mitigation of GNSS Spoofing Attacks in Maritime Environments Using a Genetic Algorithm

Saravjeet Singh, Jaiteg Singh (), Sukhjit Singh, S. B. Goyal (), Maria Simona Raboaca (), Chaman Verma and George Suciu ()
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Saravjeet Singh: Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh 140401, Punjab, India
Jaiteg Singh: Department of Computer Applications, Chitkara University Institute of Engineering and Technology, Chitkara University, Chandigarh 140401, Punjab, India
Sukhjit Singh: Department of Data Science and Mathematics, Wilfrid Laurier University, Waterloo, ON N1048, Canada
S. B. Goyal: Faculty of Information Technology, City University, Petaling Jaya 46100, Malaysia
Maria Simona Raboaca: ICSI Energy Department, National Research and Development Institute for Cryogenics and Isotopic Technologies, 240050 Ramnicu Valcea, Romania
Chaman Verma: Faculty of Informatics, University of Eötvös Loránd, 1053 Budapest, Hungary
George Suciu: R&D Department Beia Consult International Bucharest, 041386 Bucharest, Romania

Mathematics, 2022, vol. 10, issue 21, 1-20

Abstract: Due to the high reliance of daily activities on the Global Navigation Satellite System (GNSS), its security is one of the major concerns for research and industry. Most navigation and mobile-driven location-based services use GNSS to render services. Due to the low power and easy access of GNSS signals, these signals are vulnerable to spoofing and other types of attacks. Recently many GNSS spoofing attacks have been identified in road- and maritime-based environments. This study provides a technique to detect and counter the GNSS spoofing attack in the maritime environment. This technique uses the Receiver Autonomous Integrity Monitoring (RAIM) model with Least Square Estimation (LSE) and Proportional Integral Derivative (PID) Control to detect the spoofing attack. The proposed technique is based on the concept of a genetic algorithm and navigation devices, such as inertial sensors and pilot options for the ship. A case study using the AIS dataset and simulation using MATLAB and NS3 is provided to validate the performance of the proposed approach. Nine different voyages from the AIS dataset were considered to check the accuracy and performance of the proposed algorithm. The accuracy of the proposed technique was analyzed using the correctly identified attack. The result shows that the proposed technique identifies spoofing attacks with an average value of 90 percent. For result analysis the considered nine routes were traversed multiple times. Root mean square error is used to calculate the positional mismatch (error rate). Based on the combined results analysis, the average value of RMSE is 0.28. In a best-case scenario, the proposed approach provides an RMSE value of 0.009.

Keywords: inertial sensor; maritime vehicles; intelligent transportation; marine resources; rudder angle; fitness function (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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