|
Work item:
|
X.gdsml
|
|
Subject/title:
|
Guidelines for data security using machine learning in big data infrastructure
|
|
Status:
|
Under study
|
|
Approval process:
|
TAP
|
|
Type of work item:
|
Recommendation
|
|
Version:
|
New
|
|
Equivalent number:
|
-
|
|
Timing:
|
2024-09 (Medium priority)
|
|
Liaison:
|
-
|
|
Supporting members:
|
-
|
|
Summary:
|
In big data infrastructure, there are many security threats in the process of data storage, use, transmission and sharing. Current data security policy is usually static, because it does not change with the flow of data. How to dynamically and intelligently monitor, analyse, warn and respond to data security threats is a security problem to be solved in big data infrastructure. Using machine learning to enhance data security is not only helpful to improve security, but also becoming more and more urgent. It has become a necessary technology for data security protection in big data infrastructure. This Recommendation analyses the data security threats in big data infrastructure, develops a data security threat analysis framework based on machine learning, and specifies a reference model for data security threat monitoring, analysis, early warning and response using machine learning in big data infrastructure. This Recommendation provides guidelines for data security using machine learning in big data infrastructure.
|
|
Comment:
|
-
|
|
Reference(s):
|
|
|
Historic references:
|
|
Contact(s):
|
|
| ITU-T A.5 justification(s): |
|
|
|
|
First registration in the WP:
2022-06-03 17:25:54
|
|
Last update:
2024-03-12 14:18:25
|
