Flow-guided long short-term memory with adaptive directional learning for robust distributed denial of service attack detection in software-defined networking

Abstract

A software-defined networking (SDN) architecture is designed to improve network agility by decoupling the control and data planes, but while much more flexible, also makes networks more vulnerable to threats, such as distributed denial of service (DDoS) attacks. In this study we present a novel detection model, the flow-guided long short-term memory (LSTM) network with adaptive directional learning (ADL), for the mitigation of DDoS attacks in software defined networking (SDN) environments. While the methodology is based on a flow direction algorithm (FDA), which analyzes traffic patterns and detects anomalies from directional flow behavior. The proposed method integrates FDA in LSTM-based threat detection frameworks within internet of things (IoT) networks, thereby yielding enhanced detection accuracy, as well as a real-time security threat response. The experimental evaluation on two benchmark datasets, namely the InSDN dataset and a real-time dataset utilizing a Mininet and POX controller setup, shows that a detection rate of 99.85% and 99.72%, respectively, thereby showcasing the proposed model’s ability to differentiate between legitimate and malicious network traffic.