Distributed Denial of Service (DDoS) attacks represent the most common and critical attacks targeting conventional and new generation networks, such as the Internet of Things (IoT), cloud computing, and fifth-generation (5G) communication networks. In recent years, DDoS attacks have become not only massive but also sophisticated. Software-Defined Networking (SDN) technology has demonstrated effectiveness in counter-measuring complex attacks since it provides flexibility on global network monitoring and inline network configuration. Although several works have proposed to detect DDoS attacks, most of them did not use up-to-date datasets that contain the newest threats. Furthermore, only a few previous works assessed their solutions using simulated scenarios, easing the migration to production networks. This document presents the implementation of a modular and flexible SDN-based architecture to detect transport and application layer DDoS attacks using multiple Machine Learning (ML) and Deep Learning (DL) models. Exploring diverse ML/DL methods allowed us to resolve which methods perform better under different attack types and conditions. We tested the ML/DL models using two up-to-date security datasets, namely CICDoS2017 and CICDDoS2019 datasets, and they showed accuracy above 99% on classifying unseen traffic (testing set). We also deployed a simulated environment using the network emulator Mininet and the Open Network Operating System (ONOS) SDN controller. In this experimental setup, we demonstrated high detection rates, above 98% for transport DDoS attacks and up to 95% for application-layer DDoS attacks.

Available at: https://doi.org/10.1109/ACCESS.2021.3101650