Internet DRAFT - draft-kim-nmrg-2nmai5g
draft-kim-nmrg-2nmai5g
Network Management Research Group H-K. Kim
Internet-Draft M-S. Kim
Intended status: Informational SANGMYUNG UNIVERSITY
Expires: 10 January 2024 July 2023
Native Network Management using Artificial Intelligence over an Adaptive
B5G Network
draft-kim-nmrg-2nmai5g-00
Abstract
This document is derived from artificial intelligence (AI) network
and autonomous security, network management intend-based technology
to ensure constant security quality in B5G. SOAR (Security
Orchestration Automation and Response) is needed by autonomous
security and network management to optimize an adaptive B5G network.
The purpose of this document is to confirm whether the requirements
are reflected future users and developed to identify users provided
by useful decisions on how to develop the system. This document also
covers the user requirements for autonomous security and intend-based
network management to ensure constant security quality on B5G.
Status of This Memo
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Table of Contents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2
2. Convention and Terminology . . . . . . . . . . . . . . . . . 3
3. Background . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1. Terminology and Abbreviations Theorem . . . . . . . . . . 3
3.2. Autonomous Network . . . . . . . . . . . . . . . . . . . 4
4. Design of AI-based 6G Autonomous Security Control Model and
Framework Structure . . . . . . . . . . . . . . . . . . . 5
4.1. Model and framework structure for SBA structure
linkage . . . . . . . . . . . . . . . . . . . . . . . . . 5
4.2. Model and framework structure for SBMA structural
linkage . . . . . . . . . . . . . . . . . . . . . . . . . 5
4.3. Model and framework structure for AI-Enabled network
structural linkage . . . . . . . . . . . . . . . . . . . 6
4.4. NWDAF (Network Data Analytics Function) . . . . . . . . . 6
4.5. Management for other Standardization . . . . . . . . . . 7
5. B5G Native Network Management based on SOAR . . . . . . . . . 8
5.1. Purpose of B5G Native Network Management Framework . . . 8
5.2. Scope of B5G Native Network Management . . . . . . . . . 9
6. IANA Considerations . . . . . . . . . . . . . . . . . . . . . 9
7. Security Considerations . . . . . . . . . . . . . . . . . . . 9
8. Informative References . . . . . . . . . . . . . . . . . . . 9
Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . 11
1. Introduction
In order to respond to large-scale attacks on B5G communication
infrastructure based on hyper-performance, hyperspace, the advanced
security threats targeting new convergence services and intended
super-trust-based security technology. It can ensure constant
security throughout B5G infrastructure and relate to the foundational
aim to acquire skills. For native network management to optimize an
adaptive B5G network based on SOAR, there are a lot of research
fields to secure intent-based super-trust security skills and the
related technology such as vulnerability analysis and security threat
modeling to provide super-reliable infrastructure for B5G network,
AI-based autonomous security and control framework to provide safe
new convergence services in B5G, B5G-based station security to ensure
availability of 3D mobile communication and quantum security
technologies (PQC, QKD) of conversion methodology for B5G encryption
system application.
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2. Convention and Terminology
The keywords "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
"SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
document are to be interpreted as described in RFC2119.
3. Background
3.1. Terminology and Abbreviations Theorem
SDAF: Security Data Analytics Function
SBA: Service-Base Architecture
SBI: Service-Based Interface
NWDAF: Network Data Analytics Function
AF: Application Function
AMF: Access and Mobility Management Function
AOI: Area of Interest
ML: Machine Learning
MTLF: Model Training Logical Function
PCF: Policy Control Function
UPF: User Plane Function
SMF: Session Management Function
NF: Network Function
UE: User Equipment
gNB: gNodeB
SBMA: Service-Based Management Architecture
SIEM: Security Information and Event Management
SOAR: Security Orchestration, Automation, and Response
AnLF: Analytics Logical Function
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MTLF: Model Training Logical Function
3.2. Autonomous Network
The autonomous network concept is defined differently depending on
the standardization organization, and these contents are as follows.
3GPP: SON (Self-Organizing Networks)
ETS/ITU-T/GSMA: Autonomous Network
ETSI: ZSM (Zero touch network and Service Management)
Hauwei: AND (Autonomous Driving Network)
Juniper: SDN (Self-Driving Network)
Cisco: DNA (Digital(Data) Network Architecture)
Ericson: ZTN (Zero Touch Network)
Autonomous network levels can also be divided into six different
levels.
Level 0 - Manual Network: The system is supported by a monitoring
function to manually execute dynamic tasks (SNMP, CLI)
Level 1 - Assisted Network: The system executes specific and
repetitive subtasks that are preconfigured to increase execution
efficiency (Tack-Centric)
Level 2 - Partial Autonomous Network: The system enables closed-loop
O and M for specific devices based on AI models in specific external
environments (Node-Centric)
Level 3 - Conditional Autonomous network: L2-based system has
functions to detect real-time environment change, specific network
domain, and intention device. Semi-closed loop management is
possible to optimize and adjust to the external environment (Service-
Centric)
Level 4 - Highly autonomous Network: L3-based system has capabilities
of service- and customer-experience-centric in a more complex cross-
domain environment. It can analyze and make decisions based on
predictive or active closed-loop management of the network(User-
Centric)
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Level 5 - Fully autonomous network: The system is a fully autonomous
network with multi-services, multi-domains, and full lifecycle
(Value-Centric)
4. Design of AI-based 6G Autonomous Security Control Model and
Framework Structure
There are three candidates selected by conceptual design, analyzing
5G System Architecture for AI-based 6G autonomous security control
model and framework defined by the 3GPP Standardization Organization.
(1) Model and framework structure for SBA structure linkage
(2) Model and framework structure for SBMA structural linkage
(3) Model and framework structure for AI-Enabled network structural
linkage
4.1. Model and framework structure for SBA structure linkage
In this study, we aim to design at the NF level to internalize an AI-
based autonomous security control model in the SBA structure of 5G
System Architecture. There are 11 major NFs of SBA such as AMF, SMF,
UPF, NSSF, NEF, NRF, PCF, UDM, UDR, and NWDAF. Among the NFs
constituting SBA, NWDAF is used and analyzed NF that utilizes
intelligent technologies such as AI for network operation. The NWDAF
is selected as a reference model to design AI-based security analysis
functions using network data.
4.2. Model and framework structure for SBMA structural linkage
The SBMA structure defined by 3GPP into account is the Management
Plane in the SBA structure. The prior study was designed by setting
the SBA structure considering the control plane and the user plane.
We will analyze MDAS or MDAF in the SBMA structure and conduct
research and conceptual design in consideration of security
management in the future.
TBD
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4.3. Model and framework structure for AI-Enabled network structural
linkage
This structure aims to the conceptual design of element technology
for an AI-based autonomous security control model over the 6G
networks. AI-Enabled Network is designed using SBA's AI-based NWDAF.
Since NWDAF is an NF that analyzes network data for network
operations, it seems necessary for Security NF of security analysis.
SDAF is designed and analyzed using the NWDAF Wrapper method for AI-
based security of NF. NWDAF is an analysis of NF using AI and it can
consist of logical functions (AnLF and/or MTLF). It is also the
conceptual design of elemental technology for security intelligence
with the two functions of security internalization (NWDAF and SDAF).
It can need an AI Model Training for security intelligence. The
following shows the structure according to two candidate designs and
the proposed model.
TBD
4.4. NWDAF (Network Data Analytics Function)
NWDAF is one of the network functions located on the control plane in
the SBA structure of 5GC. It is based on 5G Core, MEC (Cloud), and
user equipment (UE) in edge networks. It is also used with data
collection and data analysis depending on the application function
(AF) and Operations and Administration Maintenance(OAM). The purpose
of NWDAF is to simplify the complexity of interfacing with 5GC and
3rd analytic solution providers. 5GC-related data in NWDAF is
collected with 5G network data and the data can be analyzed by
machine learning and statistical analysis. The analyzed result data
is provided to other 5G core network functions to optimize each
network function and to improve performance as its main function.
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+----+
+NF-1+----------------+--------------------+ +----+
+----+ + NWDAF +-------------+NF-1+
+-------------+ +-----+ + + +----+
+ Untrusted AF+---+NEF-1+--+ +
+-------------+ +-----+ + +--------------+ + +-----+
+----------+ + +analytic Model+ +-------------+NEF-1+
+Trusted AF+---------------+ +(Static, + + +-----+
+----------+ + + algorithm)-3 + +
+-----+ + +--------------+ +-------------------+
+UDR-1+---------------+ +
+-----+ + + +-----+
+-----+ + +-------------+OAM-2+
+OAM-2+---------------+ + +-----+
+-----+ +--------------------+
----------------------------------------------------------------------------
.................................................
. 1: Core NF 2: Network Management .
. 3: Proprietary Function .
.................................................
Figure 1: NWDAF Architecture Overview
NWDAF
+--------------------+ +--------------------+
+ AnLF + + MTLF +
+ (Analytics + + (Model Training +
+ Logical Function + + Logical Function) +
+--------------------+ +--------------------+
Figure 2: Logical Function (AnLF and/or MTLF) Structure of NWDAF
4.5. Management for other Standardization
NFV (Network Functions Virtualization) Management and NFVO (NFV
Orchestration): Identify network function (NF) lifecycle management
procedures in virtualized environments
OSM (Open Source MANO): E2E Network Service Orchestrator (NFV + Slice
+ Cross Domain)
MEC (Multi access Edge Computing) Management, F5G (Fifth Generation
Fixed Network)
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ENI (Experiential Networked Intelligence):
(a)Cognitive Netwok Management architecture using AI and Context
aware Polices
(b)Method to add intelligence to legacy systems
(c)Method between API Broker layer and Legacy System (OSS/BSS, NF,
User, etc. and ENI system)
ZSM (Zero Touch Network and Service Management):
(a)Fully Autonomous Management and Operations Framework level
(b)Management Services of Domain, Unified Fabric and Cross Domain
(c)Closed Loop Control Acquisition, Analysis, Determination and
Execution Method Actions
(D)Domain level Management, Cross Domain Level Management, Business
Service Level Management
This document present to aim B5G-based autonomous security and intend
framework based on the constant security quality guarantee to provide
the super-trusted infrastructure of the new convergent network
security service without cyber threats
5. B5G Native Network Management based on SOAR
5.1. Purpose of B5G Native Network Management Framework
It is necessary to verify native security element skill to analyze
the detailed functions such as B5G wireless access, D2D and
infrastructure virtualization. It is also needed to analyze B5G
global network security-based intelligence and internalization
technology, security vulnerability in flying base station and quantum
security for security application system. We also propose of design
of B5G native network management and requirement for B5G wireless
access/D2D/infrastructure virtualization attack model, AI-based B5G
autonomous security control of security native intelligence,
networking security and intrusion detection in flying base station
and quantum security for application of B5G security system. In
addition, security native modeling and verification are also
necessary in B5G native network management framework.
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5.2. Scope of B5G Native Network Management
Above all, the first scope is to analyze of B5G wireless access/D2D/
infrastructure virtualization elements and to define security
requirements such as B5G wireless access, Ultra-high-density of B5G
D2D and infrastructure virtualization. In next scope, there are B5G
wireless access/D2D/infrastructure virtualization attack model
development and threat analysis, design of AI-based B5G autonomous
security control and security intelligence internalization concept.
It is also necessary to design networking security, intrusion
detection element technology in flying base station and design of
quantum security technology for B5G security application.
Verification of the contents in advance is also additionally required
with the following scope.
6. IANA Considerations
There are no IANA considerations related to this document.
7. Security Considerations
[TBD]
8. Informative References
[TM-Forum] "Aaron Richard Earl Boasman-Patel, Autonomous Networks:
Empowering Digital Transformation for The Telecoms
Industry", 2019.
[ITU-T_Y.3172]
"Architectural framework for machine learning in future
networks including IMT-2020", 2020.
[ITU-T_Y.3173]
"Framework for evaluating intelligence level of future
networks including IMT-2020", 2020.
[ITU-T_Y.3174]
"Framework for data handling to enable machine learning in
future networks including IMT-2020", 2020.
[ITU-T_Y.3176]
"Machine learning marketplace integration in future
networks including IMT-2020", 2020.
[FG-ML5G_spec1]
"Requirements, architecture and design for machine
learning function orchestrator", 2020.
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[FG-ML5G_spec2]
"Machine Learning Sandbox for future networks including
IMT-2020 requirements and architecture framework", 2020.
[FG-ML5G_spec3]
"Machine learning based end to end network slice
management and orchestration", November 2020.
[FG-ML5G_spec4]
"Vertical assisted Network Slicing Based on a Cognitive
Framework", 2020.
[Y.ML_IMT2020-RAFR]
"Architecture framework for AI based network automation of
resource adaptation and failure recovery for future
networks including IMT 2020", 2020.
[TS23.288] "Architecture enhancements for 5G System to support
network data analytics services", 2021.
[TR23.791] "Study of Enablers for Network Automation for 5G", 2021.
[TR28.809] "Study on enhancement of Management Data Analytics (MDA)",
2021.
[TR28.810] "Study on concept, requirements and solutions for levels
of autonomous network", 2021.
[TR28.100] "Management and orchestration; Levels of autonomous
network", 2021.
[TR28.812] "Telecommunication management; Study on scenarios for
Intent driven management services for mobile networks",
2021.
[TR28.312] "Intent driven management services for mobile networks",
2021.
[TR28.805] "Telecommunication management; Study on management aspects
of communication services", 2021.
[TR28.535] "Management and orchestration; Management services for
communication service assurance; Requirements", 2021.
[TR28.536] "Management and orchestration; Management services for
communication service assurance; Stage 2 and Stage 3",
2021.
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[TR28.861] "Study on the Self Organizing Networks (SON) for 5G
networks", 2021.
[TR28.313] "Self-Organizing Networks (SON) for 5G networks", 2021.
Authors' Addresses
Hwan-kuk Kim
SANGMYUNG UNIVERSITY
31, Sangmyeongdae-gil, Dongnam-gu
Cheonan
Phone: +82 41 550 5101
Email: rinyfeel@smu.ac.kr
Min-Suk Kim
SANGMYUNG UNIVERSITY
31, Sangmyeongdae-gil, Dongnam-gu
Cheonan
Phone: +82 41 550 5113
Email: minsuk.kim@smu.ac.kr
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