AI-Optimized Network Function Virtualization Security in Cloud Infrastructure

Abstract

The combination of Network Function Virtualization (NFV) and cloud computing has transformed contemporary network systems since it provides a dynamic, scalable, and cost-efficient implementation of network functions. Nonetheless, this paradigm shift has come with intricate security issues caused by the aspects of multi-tenancy, virtualized environments, and decentralized infrastructures. Conventional statistical security systems are becoming inadequate to deal with the dynamics of the threat environment in NFV-enabled cloud environments. To address this gap, artificial intelligence (AI) has emerged as a game-changing methodology that provides adaptive, intelligent, and real-time threat mitigation services. The article presents the idea of using AI in NFV security systems to improve detecting, forecasting, and reacting to advanced cyber threats in the cloud. We will present a vision of an AI-optimized security architecture, where machine learning algorithms are used to detect anomalies, profile normal behavior, and automatically enforce policies across virtualized network functions. Our case studies and performance analyses demonstrate the effectiveness of AI methods in enhancing detection accuracy, reducing false positives, and providing proactive security measures. Moreover, we discuss key issues related to AI-based NFV security, such as data privacy, model explainability, and scalability, and provide insights into future work on achieving autonomous and resilient network protection systems. Our results indicate how AI can transform NFV security paradigms and enable the secure development of next-generation cloud-native networks.