提出可实时定位6G网络跨切片攻击的因果分析方法
Domain-Adapted Granger Causality for Real-Time Cross-Slice Attack Attribution in 6G Networks
- 结合资源竞争建模与因果推断，提升攻击溯源准确性
- 在1100个场景下实现89.2%准确率，响应时间小于100ms
- 结果可解释，适合自动化6G安全编排系统使用
6G网络中跨切片攻击溯源面临根本挑战：在共享基础设施环境下难以区分真实因果关系与虚假相关性。本文提出一种理论严谨的领域自适应格兰杰因果框架，将统计因果推断与网络特定资源建模相结合，实现实时攻击溯源。该方法通过引入资源竞争动态并提供形式化统计保证，解决了现有方法的关键缺陷。在具备1100个实证验证攻击场景的生产级6G测试平台上进行全面评估，取得89.2%的溯源准确率，响应时间低于100ms，相比最先进基线提升10.1个百分点，具有统计显著性。该框架提供可解释的因果说明，适用于自主6G安全编排。
原文摘要 · Abstract (English)
Cross-slice attack attribution in 6G networks faces the fundamental challenge of distinguishing genuine causal relationships from spurious correlations in shared infrastructure environments. We propose a theoretically-grounded domain-adapted Granger causality framework that integrates statistical causal inference with network-specific resource modeling for real-time attack attribution. Our approach addresses key limitations of existing methods by incorporating resource contention dynamics and providing formal statistical guarantees. Comprehensive evaluation on a production-grade 6G testbed with 1,100 empirically-validated attack scenarios demonstrates 89.2% attribution accuracy with sub-100ms response time, representing a statistically significant 10.1 percentage point improvement over state-of-the-art baselines. The framework provides interpretable causal explanations suitable for autonomous 6G security orchestration.
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