Recommendation ITU-T Y.3182 describes an intelligent cost-effective network management and orchestration framework that can cope with the challenges of multi-domain…

This Supplement analyses use cases for machine learning in future networks including IMT-2020, and presents them in a unified format.…

This Supplement presents the applications of machine learning (ML) in quantum key distribution networks (QKDNs). In particular, this Supplement includes:…

Supplement 37 to Recommendation ITU-T X.1231 defines a technical framework for countering spam based on machine learning (ML). It may…

Supplement 72 to ITU-T Y-series Recommendations provides the standardization roadmap for artificial intelligence (AI) in the field of information and…

Recommendation ITU-T M.3385 provides a detailed evaluation framework, evaluation rating method and automatic evaluating process for intelligence levels of systems…

Big data as a service (BDaaS) is a cloud service category that provides cloud service customers with capabilities to collect,…

Supplement 53 to ITU-T L-series Recommendations provides guidelines to policy-makers, technologists, innovators, environmentalists and other stakeholders from the technology industry,…

Connected vehicles are facing increasingly prominent network security issues along with their rapid development. Security threat information of connected vehicles,…

Recommendation ITU-T X.1377 establishes guidelines for an intrusion prevention system (IPS) for connected vehicles. This Recommendation mainly focuses on aspects…

Recommendation ITU-T P.1402 introduces machine-learning techniques and their application for quality of service (QoS) and quality of experience (QoE) prediction…