Godson Samwel

Comparative Evaluation of LSTM, BiLSTM, CNN-LSTM, Random Forest, and XGBoost for Detection of Distributed Denial of Service (DDoS) Attacks Using the CICDDoS2019 Dataset

The results confirm that temporal deep learning architectures substantially outperform classical ML models in detecting multi-class DDoS patterns, with the performance gap being most pronounced for low-frequency and easily confused attack classes such as DRDOS_LDAP, DRDOS_MSSQL, and UDP-LAG.