By addressing intruder attacks, network security experts work to maintain services available at all times. The Intrusion Detection System (IDS) is one of the available Mchanisms for detecting and classifying any abnormal behavior. As a result, the IDS must always be up to date with the most recent intruder attack signatures in order to maintain the confidentiality,
integrity, and availability of the services.
This project shows how the NSL-KDD dataset for Knowledge Discovery in Databases may be used to test and evaluate various Machine Learning techniques. It focuses mostly on the NLS-KDD pre-processing step in order to create an acceptable and balanced experimental data set to improve accuracy and minimize false positives .
For this study, the approaches J48 and MLP were employed. The Decision Trees classifier has been demonstrated to have the highest accuracy rate for detecting and categorizing all NSL-KDD dataset assaults.