The HYBRID SDN-IDS FRAMEWORK FOR DYNAMIC NETWORK FUNCTION VIRTUALIZATION (NFV) SECURITY

DYNAMIC NETWORK FUNCTION VIRTUALIZATION (NFV)

Abstract

Software-Defined Networking (SDN) and Network Function Virtualization (NFV) have accelerated the development of network infrastructures and brought flexibility and scalability, but they have also raised new security risks. These settings are virtualized and dynamic, and traditional security solutions are not up to the task. To improve security in NFV systems, a hybrid SDN-IDS framework combines the centralized control of SDN with the real-time threat detection powers of IDS. With the use of this framework, real-time threat mitigation, traffic rerouting, and adaptive learning are all possible, improving network visibility.

Methods: Using Principal Component Analysis (PCA) for feature extraction and Artificial Neural Networks (ANN) for classification, the framework uses a hybrid PCA-ANN model for intrusion detection. SDN-managed adaptive traffic rerouting modifies its course in response to risk scores derived from questionable network activities.

Objectives: Improvements in scalability and flexibility of network security, real-time threat detection and response, security in dynamic NFV environments, and addressing shortcomings of conventional security models are the goals.

Results: In terms of both detection accuracy and response time, the suggested framework outperformed existing techniques such as Wasserstein GAN and Web Application Firewall, achieving a 93% detection accuracy. It proved to be quite successful in dynamic NFV situations, exhibiting reduced false positive (0.05) and false negative (0.03) rates.

Conclusion: By providing a scalable, adaptable, and effective means of enhancing security in virtualized network settings, the Hybrid SDN-IDS architecture opens the door for further developments in edge computing and 5G networks.