
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.


coremsg

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-


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
