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

arXiv: https://arxiv.org/abs/2510.05165 · HTML: https://mlsanity.com/paper/2510.05165/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 15 references that are in the map.
- First posted on arXiv 4 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Granger Causality: A Review and Recent Advances](https://mlsanity.com/paper/2105.02675/) (2021)
- [6G Wireless Systems: Vision, Requirements, Challenges, Insights, and Opportunities](https://mlsanity.com/paper/2008.03213/) (2020)

## Built on by (most cited first, 2 of 2)

- [StreamSplit: Continuous Audio Representation Learning via Uncertainty-Guided Adaptive Splitting](https://mlsanity.com/paper/2605.26523/) (2026)
- [Certified Causal Attribution for Real-Time Attack Forensics in 6G Network Slicing](https://mlsanity.com/paper/2605.26679/) (2026)

## Abstract (arXiv)

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.