Native Network Management using Artificial Intelligence over an Adaptive B5G Network

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.

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.

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 MTLF: Model Training Logical Function

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) Level 5 - Fully autonomous network: The system is a fully autonomous network with multi-services, multi-domains, and full lifecycle (Value-Centric)

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

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.

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

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

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.

+----+ +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 . .................................................

NWDAF +--------------------+ +--------------------+ + AnLF + + MTLF + + (Analytics + + (Model Training + + Logical Function + + Logical Function) + +--------------------+ +--------------------+

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) 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

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.

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.

There are no IANA considerations related to this document.

[TBD]