DAD: Domain Adversarial Defense System Against DDoS Attacks in Cloud

The DDoS attack nullifies the data availability in cloud by primarily exploiting the security vulnerabilities of the network protocol and cloud services. The emergence of deep-learning based defense systems have delivered outstanding success in handling the DDoS attacks. However, the deep-learning based systems relies on having similar attack features during training and evaluation. In this paper, we propose a domain adversarial defense (DAD) system to reduce the domain mismatch and generalize the model to handle real-time attacks in cloud. Two novel strategies are applied into our proposed DAD system. 1) Latent feature extractor to extract domain invariant features across multiple domains. 2) Adversarial training algorithm to perform unsupervised adaptation over a defense system using real-time attack samples. The capability of the DAD model is empirically substantiated across four datasets to understand its effectiveness across both known and unknown attacks. The experimental analysis shows that the proposed DAD model achieves substantial improvements with minimum overhead, reduced latency and better efficacy over existing defense mechanisms.