Fully Unsupervised Detection of Physical Contacts on Subsea Cables via State-of-Polarization Monitoring
arXiv:2607.01484v1 Announce Type: cross Abstract: We present a fully unsupervised Fast-Slow DSVDD detector for continuous State-of-Polarization monitoring on a deployed subsea cable. Trained without event labels, it ranks all five confirmed trawler contacts within the top 13 of 122,174 recordings and surfaces additional corroborated cable-contact events.
The development of advanced AI techniques, specifically unsupervised learning, is enabling novel applications in critical infrastructure monitoring. This aligns with increasing geopolitical tensions and the need for enhanced security around subsea assets.
This breakthrough allows for the proactive and automated detection of physical threats to vital subsea cables, which are critical for global communication and data transfer. It improves operational resilience and reduces dependency on manual, reactive monitoring methods.
Subsea cable monitoring can now transition from reactive, often manual, incident response to proactive, AI-driven threat detection using existing fiber-optic infrastructure. This enhances security without requiring new hardware deployments.
- · Telecommunication companies
- · Subsea cable operators
- · National security agencies
- · AI/ML developers
- · Adversarial actors targeting subsea infrastructure
- · Traditional manual monitoring service providers
Increased protection and resilience for global internet and communication infrastructure, reducing the risk of disruption.
Potential for broader application of similar unsupervised AI monitoring techniques to other critical linear infrastructure like pipelines, power grids, and borders.
Enhanced confidence in the stability of global digital connectivity, potentially influencing military strategy and economic investment in regions reliant on long-distance data flow.
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Read at arXiv cs.AI