Classifying Denial of Service Attacks Using Fast Machine Learning Algorithms

Denial of service attacks are harmful cyberattacks that diminish Internet resources and services. Hence, detecting these cyberattacks is a topic of great interest in cybersecurity. Using traditional machine learning approaches in intrusion detection systems requires long training time and has high computational complexity. Thus, we evaluate performance of fast machine learning algorithms for training and generating models to detect denial of service attacks in communication networks. We use synthetically generated datasets that captured Transmission Control Protocol and User Datagram Protocol network flows in a controlled testbed laboratory environment. Evaluated algorithms include broad learning system and its extensions as well as XGBoost, LightGBM, and CatBoost gra-dient boosting decision tree algorithms. Experiments indicate that boosting algorithms often require shorter training time and have better performance.