Domain-Adapted Granger Causality for Real-Time Cross-Slice Attack Attribution in 6G Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918155435114496 |
|---|---|
| author | Quan, Minh K. Pathirana, Pubudu N. |
| author_facet | Quan, Minh K. Pathirana, Pubudu N. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05165 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain-Adapted Granger Causality for Real-Time Cross-Slice Attack Attribution in 6G Networks Quan, Minh K. Pathirana, Pubudu N. Cryptography and Security Artificial Intelligence 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. |
| title | Domain-Adapted Granger Causality for Real-Time Cross-Slice Attack Attribution in 6G Networks |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2510.05165 |