Intrusion Detection Systems (IDSs) are a noteworthy line of guard for shielding system assets from illicit infiltrations. A lot of research is being done on the development of effective Network Intrusion Detection Systems. Anomaly based Network Intrusion Detection Systems are preferred over Signature based Network Intrusion Detection Systems because of their better significance in detecting novel attacks. The research on the datasets being used for training and testing purpose in the detection model is equally concerned as better dataset quality can advance offline Intrusion Detection. Recently, Machine Learning (ML) approaches have been implemented in the Network Intrusion Detection Systems (NIDS) to protect computer networks and to overcome network security issues. This survey paper aims at disclosing different strategies followed in Intrusion Detection Systems (IDSs) using machine learning over the years and because of the headway in advancements, our lucid view is just on the most recent patterns.