Noise-Robust Multilayer Perceptron Architecture for Distributed Denial of Service Attack Detection

Abstract

Distributed Denial of Service (DDoS) attacks are one of the most challenging security threats, since a single victim is attacked by several compromised malicious nodes. As a consequence, legitimate end users can be prevented to access network resources. This letter proposes a noise-robust multilayer perceptron (MLP) architecture for DDoS attack detection trained with corrupted data. In the proposed approach, the average value of the common features among dataset instances is iteratively filtered out by applying Higher Order Singular Value Decomposition (HOSVD) based techniques. The effectiveness of the proposed architecture is validated through comparison with state-of-the-art methods.