arXiv:2605.26679v1 Announce Type: cross Abstract: 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
The proliferation of complex interconnected networks like 6G, combined with rising geopolitical tensions and advanced persistent threats, necessitates robust real-time security solutions capable of identifying sophisticated attacks.
This research addresses a critical vulnerability in future communication infrastructure by providing a certified method for rapid and accurate attack attribution, essential for maintaining network integrity and national security.
The ability to causally attribute cross-slice attacks in 6G networks within milliseconds, distinguishing genuine threats from spurious correlations, significantly enhances cybersecurity posture and resilience.
- · Telecommunications providers
- · National security agencies
- · 6G infrastructure developers
- · Cybersecurity firms
- · State-sponsored cyber attackers
- · Sophisticated hacking groups
Real-time attack forensics become a standard capability for critical infrastructure operators leveraging 6G.
Increased trust and adoption of advanced 6G network architectures due to enhanced security guarantees.
The development of 'self-healing' networks that can autonomously identify and neutralize threats based on certified causal attribution.
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Read at arXiv cs.AI