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

2026-01-09

arXiv.org

AI Summary

This work proposes the first closed-loop framework integrating causal large language models, knowledge graphs, and digital twins to overcome the limitations of existing telecom network monitoring systems, which are predominantly reactive and struggle to proactively identify complex failures such as fiber cuts or traffic overloads. By leveraging knowledge-guided generation of structured adversarial fault scenarios, the framework iteratively evaluates and refines mitigation strategies within a simulated digital twin environment. This approach enables a paradigm shift from reactive operations to predictive resilience analysis, substantially enhancing the network’s ability to anticipate and respond to high-risk cascading disruptions.

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.

Problem

• telecommunication networks
• adversarial failures
• proactive mitigation
• digital twins
• network resilience

Innovation

• Adversarial Network Imagination
• Causal LLM
• Digital Twin
• Knowledge Graph
• Proactive Mitigation