Paper Title
Network Intrusion Detection System Using Machine Learning

Abstract
Network intrusion detection systems, or NIDS, are essential for defending computer networks from several types of security threats and assaults. There is a growing need within networkattacks become more complicated and frequent. sophisticated analytics methods to improve NIDS's capacity for detection and reaction. The creation and application of analytics techniques for network intrusion detection systems is the main topic of this study. Utilizing these methods can help NIDS be more accurate, efficient, and effective at detecting and mitigating security breaches. The first part of the study looks at the foundational ideas of network intrusion detection, including the various kinds of attacks and the difficulties in detecting them. The advantages of several NIDS systems, such as signature-based and anomaly-based systems, are highlighted and restricted. The overall goal of this research is to enhance the field of network intrusion detection by using analytics approaches to increase NIDS's capabilities. The suggested techniques can decrease false positives, enhance automated incident response, process large amounts of data more effectively, and increase the accuracy of attack detection. In the face of constantly changing cyber threats, the research findings will aid in the creation of stronger and more efficient network security solutions. Keywords - Network intrusion detection systems, Analytics techniques, Security breaches, False positives, Cyber threats