Cybersecurity intrusion detection is crucial for safeguarding digital systems against malicious activities. Supervised machine learning (SML) has emerged as a powerful tool in this domain, offering effective detection capabilities. This paper explores various methods and techniques employed in cybersecurity intrusion detection using SML. It discusses the application of supervised learning algorithms, such as support vector machines, decision trees, and neural networks, in detecting intrusions. Additionally, it examines feature selection, dataset preprocessing, and performance evaluation strategies specific to SML-based intrusion detection systems. The paper also highlights current challenges and future directions in this field.