SCD: A Detection System for DDoS Attacks based on SAE-CNN Networks

Abstract

The pervasive application of network technology has given rise to a numerous of network attacks, including Distributed Denial of Service (DDoS) attacks. DDoS attacks can lead to the collapse of network resources, making the target server unable to support legitimate users, which is a critical issue in cyberspace security. In complex real-world network environments, differentiating DDoS attack traffic from normal traffic is a challenging task, making it significant to effectively distinguish between attack types in order to resist DDoS attacks. However, traditional DDoS attack detection methods have certain limitations in terms of data preprocessing and detection efficiency. In this paper, we propose a lightweight framework based on deep learning called SAE-CNN-Detection (SCD), which combines stacked autoencoder network (SAE) and convolutional neural network (CNN) for DDoS attacks detection. The CIC-DDoS2019 dataset is used to simulate network traffic that has suffered from DDoS attacks, and this system employs adaptive preprocessing techniques for the dataset. The results demonstrate that multi-classification experiment achieves an accuracy of 97.2% for DDoS attack types, while the binary classification experiment achieves an accuracy of 99.1%.