Detecting DDoS Attacks Using Adversarial Neural Network

In a Distributed Denial of Service (DDoS) attack, a network of compromised devices is used to overwhelm a target with a flood of requests, making it unable to serve legitimate requests. The detection of these at-tacks is a challenging issue in cybersecurity, which has been addressed using Machine Learning (ML) and Deep Learning (DL) algorithms. Although ML/DL can improve the detection accuracy, but they can still be evaded -ironically -through the use of ML/DL techniques in the generation of the attack traffic. In par-ticular, Generative Adversarial Networks (GAN) have proven their efficiency in mimicking legitimate data. We address the above aspects of ML/DL-based DDoS detection and anti-detection techniques. First, we propose a DDoS detection method based on the Long Short-Term Memory (LSTM) model, which is a type of Recurrent Neural Networks (RNNs) capable of learning long-term dependencies. The detection scheme yields a high accuracy level in detecting DDoS attacks. Second, we tested the same technique against dif-ferent types of adversarial DDoS attacks generated using GAN. The results show the inefficiency of the LSTM-based detection scheme. Finally, we demonstrate how to enhance this scheme to detect adversarial DDoS attacks. Our experimental results show that our detection model is efficient and accurate in iden-tifying GAN-generated adversarial DDoS traffic with a detection ratio ranging between 91.75% and 100%.(c) 2023 Elsevier Ltd. All rights reserved.