Certified Causal Attribution for Real-Time Attack Forensics in 6G Network Slicing
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arXiv
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| Format: | Preprint |
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2026
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| author | Quan, Minh K. Pathirana, Pubudu N. |
| author_facet | Quan, Minh K. Pathirana, Pubudu N. |
| contents | Cross-slice attack attribution in 6G networks requires identifying causal propagation chains through shared infrastructure in under 100 ms. Existing methods struggle to satisfy this strict SLA without sacrificing accuracy, because shared resource contention creates spurious correlations that are indistinguishable from genuine causal links under standard Granger tests. We propose DA-GC, a certified causal attribution framework that integrates resource-conditioned Granger causality with an axiomatically derived Resource Contention Model (RCM) to systematically block resource-mediated confounding. On a 15-slice production-emulation 6G testbed with 1,100 attack scenarios, DA-GC achieves 89.2% attribution accuracy at 87 ms. This represents a 7.9 percentage-point improvement over the strongest baseline at 2.7x lower latency, alongside demonstrated cross-topology generalization and concept-drift resilience. Crucially, DA-GC is backed by a comprehensive formal certification stack. We provide mathematically proven validity certificates for statistical soundness under serially dependent telemetry and piecewise-stationarity. Furthermore, we establish strict security bounds, including an adversarial utilization spoofing breakdown point of $δ^* \approx 0.95$, and define the minimum differential-privacy noise required for a provably private and robust deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_26679 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Certified Causal Attribution for Real-Time Attack Forensics in 6G Network Slicing Quan, Minh K. Pathirana, Pubudu N. Cryptography and Security Artificial Intelligence Cross-slice attack attribution in 6G networks requires identifying causal propagation chains through shared infrastructure in under 100 ms. Existing methods struggle to satisfy this strict SLA without sacrificing accuracy, because shared resource contention creates spurious correlations that are indistinguishable from genuine causal links under standard Granger tests. We propose DA-GC, a certified causal attribution framework that integrates resource-conditioned Granger causality with an axiomatically derived Resource Contention Model (RCM) to systematically block resource-mediated confounding. On a 15-slice production-emulation 6G testbed with 1,100 attack scenarios, DA-GC achieves 89.2% attribution accuracy at 87 ms. This represents a 7.9 percentage-point improvement over the strongest baseline at 2.7x lower latency, alongside demonstrated cross-topology generalization and concept-drift resilience. Crucially, DA-GC is backed by a comprehensive formal certification stack. We provide mathematically proven validity certificates for statistical soundness under serially dependent telemetry and piecewise-stationarity. Furthermore, we establish strict security bounds, including an adversarial utilization spoofing breakdown point of $δ^* \approx 0.95$, and define the minimum differential-privacy noise required for a provably private and robust deployment. |
| title | Certified Causal Attribution for Real-Time Attack Forensics in 6G Network Slicing |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2605.26679 |