AI-Enabled Learning Architecture Using Network Traffic Traces over IoT Network: A Comprehensive Review

Abstract

WiFi and private 5G networks, commonly referred to as P5G, provide Internet of Things (IoT) devices the ability to communicate at fast speeds, with low latency and with a high capacity. Will they coexist and share the burden of delivering a connection to devices at home, on the road, in the workplace, and at a park or a stadium? Or will one replace the other to manage the increase in endpoints and traffic in the enterprise, campus, and manufacturing environments? In this research, we describe IoT device testbeds to collect network traffic in a local area network and cyberspace including beyond 5G/6G network traffic traces at different layers. We also describe research problems and challenges, such as traffic classification and traffic prediction by the traffic traces of devices. An AI-enabled hierarchical learning architecture for the problems above using sources like network packets, frames, and signals from the traffic traces with machine learning models is also presented.

Artificial intelligence enabled Luong Attention and Hosmer Lemeshow Regression Window‐based attack detection in 6G

“…A detailed survey of historical review of networking technologies and the application of current trends in 6G networking was discussed in Wang et al 17 Moreover, four pivotal elements of 6G networks including, edge computing in real-time, distributed artificial intelligence, and also relevant security and privacy concerns were also detailed. A holistic and comprehensive review of AI-enabled attack detection using traces of network traffic was investigated in Aneja et al 18 From a security angle, a reassessment of prior security conventional methods is necessitated. Therefore novel authentication mechanism, encryption/decryption technique, access control, and malicious activity detection has to satisfy higher requirements of future networks.…”

“…A detailed survey of historical review of networking technologies and the application of current trends in 6G networking was discussed in Wang et al 17 Moreover, four pivotal elements of 6G networks including, edge computing in real‐time, distributed artificial intelligence, and also relevant security and privacy concerns were also detailed. A holistic and comprehensive review of AI‐enabled attack detection using traces of network traffic was investigated in Aneja et al 18 …”

Secured Wireless Network Based on a Novel Dual Integrated Neural Network Architecture

“…Tese applications are made possible by NextGen networks' [2] improved communication, processing, and artifcial intelligence (AI) capabilities. AI is one of the 34 signifcant NextG network technical advancements that must be addressed to overcome upcoming 6G networks consist of issues with security and privacy [3]. Te recently proposed innovative 6G architectural framework is prone to a number of security vulnerabilities.…”