A Deep Learning Approach to Detecting Distributed Denial of Service Attacks Using a Weighted Ensemble of Convolutional Neural Networks with Self-Attention and XGBoost, LSTM, and Random Forest Classifiers
This research paper proposes a novel deep learning model for detecting DDoS attacks that leverages a weighted ensemble of three CNN architectures enhanced with self-attention mechanisms and combined with XGBoost, LSTM, and Random Forest classifiers, demonstrating superior performance compared to traditional methods.