AI-powered deep packet inspection for secure and efficient traffic analysis in 6G programmable networks
Satish Kumar Nadendla · World Journal of Advanced Research and Reviews · 2024
Due to the rapid evolution of wireless communication, the upcoming 6G networks will eventually offer ultra-high-speed connections with ultra-low latency, as well as intelligent arrangement of the network. However, these developments pose new security and efficiency challenges that necessitate efficient detection mechanisms for traffic analysis. DPI using AI Findings can be considered as one of the best approaches towards secure and efficient management of 6G programmable networks. This paper aims to provide a theoretical foundation for the use of AI to augment DPI technologies including real-time traffic inspection, anomaly detection, and cyber defense capabilities. This article covers how, with the aid of artificial intelligence (AI) methods such as machine learning (ML) and deep learning (DL), packet inspection can be performed with high accuracy in detecting and minimizing false positives while optimizing the overall network performance. In addition, we address the role of Software-Defined Networking (SDN) and Network Function Virtualization (NFV) in enabling on-the-fly insertion of AI-augmented DPI to accommodate dynamic security needs. We propose a framework for analyzing privacy-preserving and self-evolving security models in 6G networks that utilizes federated learning (FL), reinforcement learning (RL), and explainable AI (XAI). Performance evaluation metrics such as detection accuracy, latency, and computational overhead are analyzed through simulation and actual deployments. Our work shows the promise of AI-driven DPI to serve as a building block for next generation programmable networks enabling rich and intelligent traffic governance.