Performance Evaluation In Internet Of Things Dataset Using Machine

Many Internet of Things (IoT) applications face significant hurdles in terms of data security. Machine Learning (ML)-based intrusion detection systems (IDS) claim to be effective and accurate at analysing network data and detecting threats. Our suggested technique, nweighted-univariate feature selection, creates a threshold value that serves as a weight, from which critical features are extracted and then used to machine learning algorithms like support vector machine (SVM) and decision tree (DT). These models were trained using the UNSWNB- 15 dataset, which was developed in the Australian Center for Cyber Security's Cyber Range Lab using an IXIA PerfectStrom tool (ACCS). It has a mix of realistic modern normal and contemporary network traffic assault characteristics. Accuracy, precision, and recall were used to evaluate the performance of our suggested model. In DT, the proposed model has a greater accuracy of 96.4 than SVM, which has an accuracy of 89.1.