Securing SDN Networks: Employing LSTM and Linear SVM Models for Enhanced Network Security with VPN Integration

Abstract

In the ever-changing landscape of network security, Software-Defined Networking (SDN) is a critical foundation that requires strong protections against cyber threats. This research describes a novel strategy to fortifying SDN networks by combining Deep Learning (DL) and Machine Learning (ML) approaches. Our solution detects, analyses, and prevents potential security breaches in real time by using Long Short-Term Memory (LSTM) and Linear Support Vector Machine (SVM) models, as well as PyVPN integration. Our technology intends to improve SDN network resilience against a variety of cyber threats, including malware, intrusions, and denial-of-service attacks, by analysing network traffic patterns comprehensively and proactively identifying anomalies. Through extensive validation, we demonstrate the usefulness of our strategy in strengthening SDN network security, providinga robust defense mechanism against the everpersistent threat landscape.