ABSTRACT
Nowadays, the IoT is the most advanced technologies of the 21st century. As this technology advances, attacks are also growing in parallel. One of the most dangerous attacks IoT devices face is the DDoS attack. This paper addresses DDoS attack detection and other types of attacks to which IoT devices are vulnerable. We evaluated the CICIoT2023 dataset to analyze various DDoS attacks. In this work, we developed a novel hybrid feature selection algorithm combining SelectKBest and RFE. With a hybrid, algorithm, we selected the top 10 features and trained them with machine learning algorithms. We applied four models, RF, DT, XGB, and KNN to evaluate the CICIoT2023 dataset in two categories: 34 classes and 12 DDoS classes, using 5, 10, and 15 features. For the 34 classes, we achieved 99.15% accuracy, and for the 12 DDoS classes, we achieved 99.95% accuracy with the Decision tree model.
