In the modern world, information security and communications concerns are growing due to increasing attacks and abnormalities. The presence of attacks and intrusion in the network may affect various fields such as social welfare, economic issues and data storage. Thus intrusion detection (ID) is a broad research area, and various methods have emerged over the years. Hence, detecting and classifying new attacks from several attacks are complicated tasks in the network. This review categorizes the security threats and challenges in the network by accessing present ID techniques. The major objective of this study is to review conventional tools and datasets for implementing network intrusion detection systems (NIDS) with open source malware scanning software. Furthermore, it examines and compares state-of-art NIDS approaches in regard to construction, deployment, detection, attack and validation parameters. This review deals with machine learning (ML) based and deep learning (DL) based NIDS techniques and then deliberates future research on unknown and known attacks.