Open-Source IDS/IPS Using Statistical and Deep Learning Based Anomaly Detection in SDN

Abstract

This paper explores the implementation of open-source Intrusion Detection and Prevention Systems (IDS/IPS) using statistical and deep learning-based anomaly detection techniques in Software Defined Networking (SDN) environments. It aims to address the security challenges in cloud computing by proposing a hybrid approach that combines signature-based and anomaly-based detection methods. Our results demonstrate significant improvements in detection rates and reduced false positives compared to existing systems.