Evaluation of Machine Learning Models for Intrusion Detection with the UNSW-NB15 Dataset

Network Intrusion Detection System has become a crucial component of the Internet of Things (IoT) framework for expanding Internet security problems. Most intrusion detection research in the past used the KDDCUP99 dataset for testing. However, certain prevalent instances still need to be included when comparing the KDDCUP99 dataset to the UNSWNB15 dataset to evaluate Network Intrusion Detection System. For classification studies, we present Random Forest, Logistic Regression, and Support Vector Machine techniques in this study. Our method’s effectiveness is assessed using Precision, F1-Score, Recall, and accuracy. The findings are evaluated in light of previously conducted studies. The experimental findings demonstrate the suggested Random forest method provides better results than alternative approaches.