DDoS Detection Using Hybrid Deep Neural Network Approaches

Abstract

In this study, we provide Deep Neural Network (DNN) based approaches to detecting Distributed Denial-of-Service (DDoS) attacks. In order to improve the DNN’s accuracy, the suggested approaches use two different hybrid DNN scenario detections to demonstrate the possibilities. As training and testing data, we use the publicly available Intrusion Detection datasets; CIC-IDS2017 and CIC-DDoS2019. Experiments have shown that the presented approaches are 99.9% effective at detecting attacks.