Deep Learning-Based Intrusion Detection for Secure Software Defined Networks

Abstract

Software Defined Networking (SDN) provides improved flexibility, programmability, and centralized control for modern network management. However, ensuring network security in SDN environments remains a challenging task due to dynamic traffic patterns and evolving cyber threats. Traditional intrusion detection approaches often fail to adapt to complex and changing attack behaviors, resulting in reduced detection accuracy and increased vulnerability. This paper presents a deep learning-based intrusion detection framework for SDN environments using a hybrid modeling approach. The proposed method analyzes network traffic patterns and flow-level features to identify malicious activities effectively. By leveraging both spatial and temporal characteristics of traffic data, the framework enhances detection capability and improves decision-making at the SDN controller. Experimental evaluation demonstrates that the proposed approach achieves better detection performance and overall system efficiency compared with conventional intrusion detection methods.