Auto Configured Mechanism for Detecting Malicious Attacks on Sensitive Data in Software Defined Network Controller

Abstract

The rapid growth of Software-Defined Networking (SDN) has introduced new security challenges due to its centralized control architecture and programmable interfaces, making it an attractive target for cyberattacks. Threats such as denial-of-service attacks, impersonation, and unauthorized access can severely compromise network performance and data integrity. This study proposes an intelligent and adaptive controller-based framework to enhance SDN security through automated configuration, discovery, and protection mechanisms within the control plane. The proposed framework integrates real-time traffic monitoring, anomaly detection, and policy-driven Access Control List (ACL) enforcement directly into the SDN controller. A simulation-based experimental methodology was employed using the Mininet network emulator and the POX controller to model realistic network topologies and attack scenarios. Key performance metrics, including throughput, latency, packet delivery ratio, CPU utilization, and detection accuracy, were used to evaluate the effectiveness of the framework. Comparative analysis against baseline SDN configurations and existing security approaches was also conducted. Experimental results show that the proposed framework successfully detects up to 90% of malicious traffic, significantly reduces controller overhead, and achieves higher detection accuracy while maintaining acceptable network performance. These findings demonstrate that controller-based automated security mechanisms are efficient, scalable, and essential for resilient programmable network environments.