Description

Machine Learning Algorithms-based “Network Intrusion Detection System” is a soft device that monitors computer networks to detect malicious activity that is intended to steal confidential information or damage / hack network agreements. This work presents Reliable-NIDS (R-NIDS), a novel methodology for Machine Learning (ML) based Network Intrusion Detection Systems (NIDSs) that allows ML models to work on integrated datasets, empowering the learning process with diverse information from different datasets. Therefore, R-NIDS targets the design of more robust models that generalize better than traditional approaches. We propose a new dataset, called UNK22. It is built from three of the most well-known network datasets (UGR’16, USNW-NB15 and NLS-KDD), each one gathered from its own network environment, with different features and classes, by using a data aggregation approach present in R-NIDS. In this process we proposes the Machine learning method such as XGBoost, Random Forest and Logistic Regression used to test IDS. Finally it will generates the result in the form of metrics like accuracy, precision, recall and f1-score.