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The detection of possible intrusions to any networking system is always considered as the most important task especially when the role of attacks has changed on NIDS. The improved modeling implementation on NIDS can only be archived by making Machine learning implementation on intrusion data for detection of possible intrusions. The implementation of machine learning techniques can be possible on IoT networks, cloud implementation, traditional networks or even on the WSN's for identification of possible intrusions. In this article, the possible challenges which can create barriers to the detection and mitigation of possible intrusions are identified with help of ML techniques and intrusion datasets which are available for public uses. The possible problems associated to implementation of NIDS are mainly explained in this article. The improved development of NIDS models will become possible to the researchers with help of this article. [ABSTRACT FROM AUTHOR]