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

TL;DR

This paper introduces a proactive framework combining causal LLMs, knowledge graphs, and digital twins to simulate and evaluate network failures, enabling anticipatory resilience in telecommunication systems.

Contribution

It presents a novel closed-loop system that generates and tests failure scenarios proactively, improving upon reactive monitoring methods in telecom networks.

Findings

Successfully simulates complex failure scenarios

Enables evaluation of mitigation strategies in digital twin environment

Shifts network management from reactive to proactive

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.