An Efficient ML-Based Model for Network Intrusion Detection System

In the modern era, cyber-security is a solution to protect systems, networks, and programs from different attacks. A network intrusion detection system (NIDS) is a secure system that analyses vulnerabilities and security attacks in cyberspace which is used to find malicious activity. Machine Learning (ML) techniques are frequently used to solve anomaly-related problems. Therefore, a data-driven machine learning model is proposed to detect the issues related to NIDS through the benchmark dataset. The study's results depict that the proposed model outperforms the contemporary model.