| Guidelines for data security using machine learning in big data infrastructure |
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In big data infrastructure, there are many security threats in the process of data collection, storage, processing, and management. How to dynamically and intelligently monitor, analyse, and respond to data security threats is a problem to be solved in big data infrastructure. Machine learning has the capabilities of automatic learning and pattern recognition, which can discover potential security threats and abnormal behaviours from a large amount of data so that proactive defensive measures can be taken. Using machine learning to enhance data security has gradually become a necessary technology for big data infrastructure.
Recommendation ITU-T X.1753 analyses data security threats in big data infrastructure and scenarios where machine learning can be applied to data security protection in big data infrastructure and provides guidelines for using machine learning to protect data security of big data infrastructure.
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ITU-T Recommendation |
Status |
Summary |
Table of Contents |
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1
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X.1753 (12/2025)
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In force
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here
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here
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here
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Approved on |
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Guidelines for identity-based cryptosystems used for cross-domain secure communications
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2023
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here
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Overview of hybrid approaches for key exchange with quantum key distribution
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2022
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here
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Guidelines for security management of using artificial intelligence technology
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2022
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here
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Successful use of security standards (2nd edition)
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2020
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here
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Description of the incubation mechanism and ways to improve it
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2020
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here
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Strategic approaches to the transformation of security studies
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2020
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here
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Security considerations for quantum key distribution networks
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2020
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here
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