JAMMER DETECTION IN AUTONOMOUS VEHICLES WITH MACHINE LEARNING

Date

2025-1-23

Author

Demiryürek, Mert

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Wireless communication systems of autonomous vehicles may be vulnerable to threats such as jammer attacks. As a result of these attacks, operational failures and security problems may occur. This study aims to classify which jammer attacking scenario occurs, using machine learning in order to select the necessary precaution to be taken against jammer attacks. A simulated dataset consisting of parameters such as RSSI, SNR, PDR and estimated relative speed was used in the study. KNN, Random Forest and XGBoost models were used for jammer detection and their performances were compared. The results showed that all of the models, KNN, Random Forest, and XGBoost models has similar accuracy results which is above 90%. The results show that accurate detection of jammer attacks can be achieved with the machine learning algorithms

Subject Keywords

Machine Learning, Jammer Detection, Wireless Communication, KNN, Random Forest, XGBoost

URI

https://hdl.handle.net/11511/113065

Collections

Graduate School of Informatics, Term Project