Global Navigation Satellite Systems (GNSS) are often the target of malicious attacks and interferences, mainly spoofing, thus posing a significant threat to both civilian and military equipment, and therefore necessitating effective detection and identification of such attacks. In this 'Work-in-Progress' paper, we propose the application of Machine Learning neural networks, a methodology proven highly effective in fields like cyberattack detection, to identify spoofing events across various scenarios. Our approach consists in computing non-time related metrics from a dataset of known spoofed signals, using the observables and signal-level measurements provided by a GNSS software receiver. The training is validated on both spoofed and clean scenarios to ensure a comprehensive approach. Furthermore, we provide a description of the feature's importance in the decision-making process of the model.