Domain-Adapted Granger Causality for Real-Time Cross-Slice Attack Attribution in 6G Networks
This addresses real-time attack attribution for 6G network security, representing a domain-specific incremental improvement.
The paper tackles the problem of distinguishing genuine causal relationships from spurious correlations for cross-slice attack attribution in 6G networks, achieving 89.2% attribution accuracy with sub-100ms response time, which is a 10.1 percentage point improvement over state-of-the-art baselines.
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.