earticle

영어

The development of the Internet of Things (IoT) has created new issues in network security due to the diverse resource-constrained nature of IoT devices and the massive volume of generated heterogeneous data. One of the most important procedures in network security is intrusion detection system (IDS). The goal of intrusion detection is to locate and stop harmful activity within the network. Machine learning methods are employed to create precise IDS models. This article provides a practical overview of tree-based Machine Learning (ML) algorithms for intrusion detection. It delves into the application of Random Forest (RF), Decision Tree (DT), AdaBoost, and the J48 classifier in the context of network traffic security. The NSL-KDD data set is used to evaluate these approaches. According to experimental results, the Random Forest Classifier outperforms the other techniques.