用因果大模型预演网络故障，提前测试应对方案
Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation
- 用因果大模型结合知识图谱生成真实依赖的故障场景
- 在数字孪生中模拟故障，量化性能下降并评估对策
- 闭环优化故障设想，适合电信运维与安全团队
通信网络常遭遇光缆断裂、流量拥塞和级联中断等复杂故障。现有监控与数字孪生系统多为被动响应，仅在服务降级后才检测故障。本文提出对抗性网络想象框架，整合因果大语言模型（Causal LLM）、知识图谱与数字孪生，主动生成、模拟并评估对抗性网络故障。因果LLM基于知识图谱中的网络依赖关系生成结构化故障场景，这些场景在数字孪生中执行，用于衡量性能退化并评估缓解策略。通过根据仿真反馈迭代优化故障场景，该框架将网络运维从被动排障转向主动韧性分析。
原文摘要 · Abstract (English)
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.
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