Domain-Adapted Granger Causality for Real-Time Cross-Slice Attack Attribution in 6G Networks

2025/10/04 by Quan, Minh K., Pathirana, Pubudu N.
#Artificial Intelligence (cs.AI) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2510.05165

Abstract

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.

Citations

• Granger Causality: A Review and Recent Advances
• 6G Wireless Systems: Vision, Requirements, Challenges, Insights, and\n Opportunities
• Inductive Representation Learning on Large Graphs
• Neural Machine Translation by Jointly Learning to Align and Translate

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