Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation
This addresses network reliability for telecom operators, but it appears incremental as it builds on existing digital twin and monitoring systems.
The paper tackles the problem of reactive failure detection in telecommunication networks by proposing Adversarial Network Imagination, a framework that proactively generates and simulates adversarial failure scenarios using a Causal LLM, Knowledge Graph, and Digital Twin, resulting in a shift toward anticipatory resilience analysis.
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.