Adversarial attacks against multi-layer perceptron on DDoS data

Abstract Multi-Layer Perceptrons are widely used for Distributed Denial-of-Service detection due to their high accuracy in distinguishing malicious from benign traffic. However, their vulnerability to adversarial perturbations, subtle input modifications that evade detection, remains largely unexplored which poses significant risks for real-world deployment. This study evaluates the susceptibility of a state-of-the-art Multi-Layer Perceptron model (Sharif et al. in IEEE Access 11:51810–51819, 20