The limitations of traditional technologies like firewalls and the advancement of technology lead to an increasing need for new technologies and more advanced cyber security solutions. Network security is seriously jeopardized by these variables. Previous studies have demonstrated that a wide range of intrusion detection technologies, including those employed in DoS (Denial of Service), Probe, U2R (User to Root), as well as R2L (Remote to Local) attacks, are mainly inefficient at identifying and classifying network attacks. For better assessment and identification, this study projected an intrusion recognition system model depending on a machine learning algorithm. The model is built using Knowledge Discovery in Databases, or KDD for short, which is a highly recommended intrusion detection system technique.