With the advancement of networking applications, the need for security to resolve malicious activity in the network has increased. Network intrusion detection has evolved as a significant security system in networks, enabling it to detect unauthorized access to any network traffic. Through network intrusion systems, a warning message was attained to take necessary action to avoid malicious attacks. However, there is still the need for improvement in network intrusion system since the advancement in technology has created complexity over the detection system, making the current detection system is not effective. Intrusion Detection System (IDS) usually operates based on a trained network traffic pattern. It is defined in such a way that if there exist any variant on the traffic pattern, intrusion will be detected. We need a solution to avoid network attacks, which can be achieved with IDS. Machine Learning (ML) algorithms play a key role in all sectors and domains. In this paper, we investigated the various supervised machine learning algorithms such as Naive Bayes, Random Forest, SVM and XGBoost, and the performance of each algorithm concerning accuracy. This study helps in finding a suitable algorithm to identify the attacks with more accuracy. We used the standard intrusion dataset, i.e. NSLKDD from Canadian Institute for Cyber Security.