Generalized Jamming Detection in NR V2X Using Gaussian Mixture Model

In intelligent transportation systems, highly reliable information exchange via New Radio (NR) Vehicles-to-Everything (V2X) communications is pivotal for ensuring road safety and enhancing traffic efficiency. However, the open nature of wireless channels renders NR V2X communications highly vulnerable to interference, thereby presenting opportunities for potential attackers to exploit. This paper proposes a method based on Gaussian Mixture Models (GMM) for detecting potential jamming attacks in NR V2X communications. This method detects jamming employing parameters calculated by stochastic geometry, without relying on abundant datasets. By utilizing the expectation-maximization algorithm for iterative, this method is able to identify jamming attacks based on the measured power. Extensive numerical analysis has demonstrated that our proposed method exhibits superior accuracy compared to existing schemes. Besides, this method is general, and capable of detecting jamming attacks under unknown jammer characteristics and varying vehicle densities.