A DDoS Detection Model Based on Feature Construction and Deep Forest
Shuangquan Zhang, Yunpeng Sun, Jiahui Fei, Rui Zhang, Peng Gao, Zhichao Lian · 2024
DDoS attacks websites and servers by disrupting network services in an attempt to drain the application's resources. With the explosive growth of electric vehicles (EVs), DDoS attacks have threatened the security of EVs and charging stations. In this work, we developed a novel machine learning model named FCDForest to detect DDoS attacks on the CICEV 2023 dataset. FCDForest employs feature construction and deep forest to detect DDoS attacks on the CICEV 2023 dataset. The feature construction is used to construct features, and the deep forest is used as the classification model to detect DDoS attacks. This study selected six existing models as comparison models of FCDForest. In light of our experiments, FCDForest achieved the highest accuracy of 0.94 on the CICEV 2023 dataset. Our experiments indicated that FCDForest is feasible for DDoS attack detection, and feature construction method can improve models’ performance on DDoS attack detection.