Analysis of network attack classification success and extraction of the most important features

The rapid increase in the number of Internet of Things (IoT) devices has raised security concerns due to their simplistic architectures. Attacks on these devices can compromise user privacy and disrupt critical infrastructure. This paper focuses on selecting key features from the UNB CIC IOT 2023 dataset to improve network attack classification success using machine learning algorithms and evaluates the effectiveness of existing network monitoring tools in detecting these features. The XGBoost algorithm achieved the highest F1 score of approximately 0.93.