In this paper, the prime focus is to find and explore the field of intrusion detection with supervised ML approaches. The objective is to create and publish a scientific classification for combined IDS and supervised ML techniques. To do the same, examination of the fundamentals of IDS, ML algorithms (supervised), and cyber attacks is done. In addition, the work has been explored that has recently been done in the sphere of supervised learning intrusion detection. Then, using these works, a taxonomy is provided using different publicly available datasets to classify the high and accurate performance of supervised machine learning algorithms.