Abstract:

Global Navigation Satellite Systems (GNSSs) are the specific term utilized with satellite constellation to acquire regional or global services. GNSS sensors use pseudo-distance measurement to estimate the position, velocity, and time (PVT). Several GNSS devices are exposed to detect spoofing attacks due to the use of unsafe locations. In addition, misleading signals are intentionally used to generate timing and position, and GNSS signal spoofing provides a constant risk to consumers. In past works, the implementation of the Global Positioning System (GPS) in autonomous vehicle navigation might be endangered by spoofing. To mitigate these issues, this task develops a hybrid machine-learning method for mitigating and detecting GNSS spoofing attacks. The developed model is processed with three phases: data collection, feature extraction, and detection. Initially, the required data is taken from the standard resource. Then, the data is given to the feature extraction phase. The features of the data are retrieved using the principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) model. The features obtained from the collected data are transferred to the detection phase. In the final phase, the GNSS spoofing detection and mitigation is executed using a machine learning method called as hybridized adaptive Bayesian learning and multi-layer perceptron (HABMLP). Enhanced osprey optimization algorithm (EOOA) is utilized for optimizing the variables to enhance the efficacy of models and achieves greater performance than other standard models.

Key words: Global Navigation Satellite System (GNSS), detection and mitigation of GNSS, t-distributed stochastic neighbor embedding (t-SNE), enhanced osprey optimization algorithm, principal component analysis (PCA), hybridized adaptive Bayesian learning and multi-layer perceptron (HABMLP)