Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation

Vignesh Sriram, Yuqiao Meng, Luoxi Tang, Zhaohan Xi

TL;DR

Adversarial Network Imagination is proposed, a closed-loop framework that integrates a Causal Large Language Model, a Knowledge Graph, and a Digital Twin to proactively generate, simulate, and evaluate adversarial network failures.

Abstract

Telecommunication networks experience complex failures such as fiber cuts, traffic overloads, and cascading outages. Existing monitoring and digital twin systems are largely reactive, detecting failures only after service degradation occurs. We propose Adversarial Network Imagination, a closed-loop framework that integrates a Causal Large Language Model (LLM), a Knowledge Graph, and a Digital Twin to proactively generate, simulate, and evaluate adversarial network failures. The Causal LLM produces structured failure scenarios grounded in network dependencies encoded in the Knowledge Graph. These scenarios are executed within a Digital Twin to measure performance degradation and evaluate mitigation strategies. By iteratively refining scenarios based on simulation feedback, the framework shifts network operations from reactive troubleshooting toward anticipatory resilience analysis.

Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation

TL;DR

Adversarial Network Imagination is proposed, a closed-loop framework that integrates a Causal Large Language Model, a Knowledge Graph, and a Digital Twin to proactively generate, simulate, and evaluate adversarial network failures.

Abstract

Telecommunication networks experience complex failures such as fiber cuts, traffic overloads, and cascading outages. Existing monitoring and digital twin systems are largely reactive, detecting failures only after service degradation occurs. We propose Adversarial Network Imagination, a closed-loop framework that integrates a Causal Large Language Model (LLM), a Knowledge Graph, and a Digital Twin to proactively generate, simulate, and evaluate adversarial network failures. The Causal LLM produces structured failure scenarios grounded in network dependencies encoded in the Knowledge Graph. These scenarios are executed within a Digital Twin to measure performance degradation and evaluate mitigation strategies. By iteratively refining scenarios based on simulation feedback, the framework shifts network operations from reactive troubleshooting toward anticipatory resilience analysis.
Paper Structure (34 sections, 6 figures, 2 tables)

This paper contains 34 sections, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Causal Modeling and Knowledge Graphs
  4. LLMs for Reasoning and Structured Generation
  5. Background and Problem Definition
  6. Method
  7. Causal Language Modeling
  8. Causal LLM
  9. Knowledge Graph
  10. Adversarial Scenario Generator
  11. Digital Twin Execution Engine
  12. Mitigation Engine
  13. Workflow Summary
  14. Experiments
  15. Experimental Settings
  16. ...and 19 more sections

Figures (6)

  • Figure 1: Overview of the proposed Adversarial Network Imagination framework.
  • Figure 2: Mini Knowledge Graph of a telecom network illustrating routers, services, traffic flows, and their dependencies.
  • Figure 3: Overview of the Adversarial Network Imagination framework. The Causal LLM generates failure scenarios using the Knowledge Graph, which are executed in the Digital Twin and assessed by the Mitigation Engine. Feedback refines scenario generation in a closed loop. Operators interact with the framework by configuring scenario constraints and inspecting simulated outcomes, while all failure generation and evaluation remain fully automated within the causal loop.
  • Figure 4: Qualitative comparison of adversarial scenario generation and mitigation effectiveness.
  • Figure 5: Ablation Study Effectiveness.
  • ...and 1 more figures