PANews|3月 14, 2026 03:52
**[Cambridge Study: Simultaneous Disruption of 72-92% of Submarine Cables Needed to Significantly Impact Bitcoin Network]**
According to CoinDesk, a study published by the Cambridge Centre for Alternative Finance (CCAF) reveals that the Bitcoin network's resilience to physical infrastructure disruptions is far greater than previously anticipated. Based on 11 years of network data and analysis of 68 verified submarine cable failure incidents, the study found that in the case of random failures, simultaneous disruption of 72% to 92% of international submarine cables would be required to significantly affect Bitcoin node connectivity.
The study pointed out that among the 68 real-world submarine cable failure incidents analyzed, over 87% of the events impacted less than 5% of nodes. For example, in March 2024, submarine disturbances off the coast of Côte d'Ivoire damaged 7 to 8 cables simultaneously, affecting approximately 43% of local regional nodes. However, globally, only about 5 to 7 Bitcoin nodes were impacted, accounting for roughly 0.03% of the network. The Bitcoin network is notably more vulnerable to targeted attacks than random failures. If attackers were to disrupt critical communication hub cables, impacting approximately 20% of key lines could cause similar disruptions. Coordinated interruptions targeting hosting providers with the highest number of nodes (such as Hetzner, OVH, Comcast, Amazon, and Google Cloud) could result in significant impacts with just a 5% reduction in routing capacity.
Additionally, the study found that Tor network nodes accounted for approximately 64% (as of 2025), with their distribution primarily concentrated in connectivity-strong countries such as Germany, France, and the Netherlands. This concentration paradoxically enhances the overall resilience of the network. The study suggests that the Bitcoin community is increasingly adopting more censorship-resistant infrastructure in response to internet censorship and geopolitical events, giving the network an "adaptive reinforcement" characteristic.
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