DDoS attack detection and mitigation using deep neural network in SDN environment

Abstract

In the contemporary digital landscape, the escalating threat landscape of cyber attacks, particularly distributed denial-of-service (DDoS) attacks, has become a paramount concern for network security. This research introduces an innovative approach to DDoS detection leveraging a deep neural network (DNN) architecture rooted in deep learning (DL) principles. The proposed model exhibits a scalable and adaptable framework, enabling meticulous analysis of network traffic data to discern intricate patterns indicative of DDoS attacks. To validate the efficacy of our methodology, rigorous evaluations were conducted using authentic real-world traffic data. The results unequivocally establish the superiority of our DNN-based approach over traditional DDoS detection techniques. This research holds significant promise for bolstering network security, particularly within the dynamic landscape of software-defined network (SDN) environments. The study's findings contribute to the continual refinement and eventual deployment of advanced measures in fortifying digital infrastructure against the evolving threat landscape. Performance metrics, including detection accuracy and loss rates, further emphasize the effectiveness of our approach across different datasets. With detection accuracy rates of 99.98%, 100%, and 99.99% for the InSDN, CICIDS2018, and Kaggle DDoS datasets, respectively, coupled with low loss rates, our DNN-based model demonstrates robust capabilities in mitigating contemporary DDoS threats. This study not only presents a novel DDoS detection approach within SDN infrastructures but also offers insights into practical implications and challenges associated with deploying DNNs in real-world SDN environments. Network security professionals can benefit from the nuanced perspectives provided, contributing to the ongoing discourse on fortifying digital networks against evolving cyber threats.