This paper examines the application of deep learning (DL) techniques for forecasting DDoS attacks (i.e., predicting future attacks in advance). We used a uniform modeling approach that enabled us to compare the performance of various DL algorithms, specifically Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). The results indicate that both short-term (a few seconds ahead) and mid-term (up to 20 seconds ahead) DDoS traffic prediction with very high accuracy are possible. Additionally, the results show that the LSTM model outperforms both RNN and GRU, as well as machine learning algorithms such as Support Vector Machines (SVM), Random Forests (RF), and k-Nearest Neighbors (KNN).
Denial-of-service attackInformatiqueDeep learningCybersecurityArtificial intelligenceThe Internet