Towards 6G: Intelligent and Secure Intent-based Networking for Autonomous Network Management

Summary:

6G network is expected to support beyond communication services such as computing, storage, control, sensing, localization, and robotics, which enable the development of a wide range of diverse services and applications. The increasing complexity and dynamic requirements of 6G networks call for new management and security paradigms, such as intent-based networking (IBN) and zero-touch security management. This tutorial introduces the concepts of IBN, describes its architecture, components, and enabling technologies, defines the implementation challenges, and explores potential solutions. It also explores the role of artificial intelligence (AI) in empowering intent-based and security management paradigms, highlighting its potential, and addressing associated challenges. Participants in this session will gain insights into the current research and standardization efforts aimed at enabling zero-touch security management, the potential of emerging AI techniques in identifying and mitigating sophisticated network security threats, as well as the challenges posed by adversarial attacks targeting AI-powered systems themselves. The opportunities and challenges of AI-driven security will be illustrated through concrete proof-of-concept implementations.

Objectives and Motivation:

First Part: Intent-Based Networking in 6G: Concepts, Architecture, Challenges, and Solutions.

• Objectives
  • Educate participants on IBN concepts.
  • Explain the IBN architecture and components.
  • Introduce the main challenges in IBN implementation.
  • Explore the potential solution for IBN implementation.
  • Introduce the role of AI/ML in IBN.
  • Demonstrate IBN concepts through case studies.
• Motivations
  • Address the complexity of future communication networks.
  • Reduce the gap between academia and industry.
  • Leverage AI/ML for network automation.

Second Part: AI-powered Zero-touch Security Management in Cloud-Native Mobile Networks.

• Objectives
  • Introduce participants to zero-touch security management.
  • Demonstrate AI’s role in empowering zero-touch security management.
  • Examine security challenges stemming from AI integration.
• Motivations
  • Addressing the complexity of future mobile networks security.
  • Leveraging AI for enhanced security management.
  • Advancing towards robust and trustworthy AI-driven security management.

Presenters:

First part (1.5 hour): Mehdi Bennis and Salwa Mostafa
Centre for Wireless Communications, University of Oulu, Finland.

Second Part (1.5 hour): Chafika Benzaid
Centre for Wireless Communications, University of Oulu, Finland.

Target Audience:

students, postdoctoral researchers, senior researchers, network professionals, AI researchers, and professors.

Outline of Tutorial:

First Part: Intent-Based Networking in 6G: Concepts, Architecture, Challenges, and Solutions.

• What is intent-based networking?
  • Explain IBN and why we need it.
  • Compare between traditional networks and IBN.
  • Introduce IBN architecture and components.
  • Illustrate the enabling technologies for IBN.
  • Define the main challenges in IBN implementation.
  • Shed light on potential solutions for the main challenges.
• What is the role of AI/ML in IBN?
  • Overview of AI/ML techniques used in IBN.
  • Case Study (1): Emergent communication for IBN.
  • Case Study (2): LLM for IBN.
• Q&A and Interactive Discussion.

Second Part: AI-powered Zero-touch Security Management in Cloud-Native Mobile Networks.

• Introduction to Zero-touch Security Management
  • Design principles and key enabling technologies of zero-touch security management.
  • Standardization initiatives to enable the zero-touch management concept.
  • Illustrative zero-touch 5G/6G security management frameworks.
• Role of AI in Empowering Zero-touch Security Management
  • Overview of the potential of AI in the different stages of security management closed loop.
  • PoC: Emerging AI/ML techniques for detecting and mitigating sophisticated network attacks.
• AI’s Security Challenges
  • Overview of the adversarial threat landscape.\
  • Case studies demonstrating adversarial attacks against AI-powered solutions in 5G/6G net-
    works.
  • Potential defenses to empower robust and trustworthy AI.
• Q&A and Interactive Discussion.