@inproceedings{frankenSubSeaCureGridBasedRisk2026,
title = {{SubSeaCure}: {Grid}-{Based} {Risk} {Mapping} of {Europe}’s {Subsea} {Data} {Cable} {Network}},
abstract = {Abstract. Subsea data cables carry the vast majority of intercontinental data traffic, yet existing regional risk assessments have relied on simulated inputs or narrow-area studies. SubSeaCure provides a grid-basedmethodology that uses observational data to quantify and visualize external risks to subsea data cables across Europe’s adjacent seas. Six risk factors (fishing, anchorage, dredging, seabed ruggedness, volcanic activity, and seismic activity) were mapped to a raster grid and normalized to a common scale. Using a weighted-sum model derived from an analytic hierarchy process, the factors were aggregated into a combined risk score dominated by fishing and anchorage as the most prevalent causes of cable incidents. The resulting risk map revealed pronounced spatial heterogeneity. The most extensive high-risk belt spans the North Sea and English Channel, with particularly elevated risk around Denmark, Germany, and the Netherlands. Further high-risk concentrations were also observed in the northeastern Mediterranean. Offshore, deeper Atlantic areas, and parts of the Black Sea exhibited comparatively low risk levels. These results provide contextual input for public and private investment considerations, route engineering, targeted protection and burial, surveillance prioritization, or plausibility checks during incident assessment. The SubSeaCure methodology is scalable and provides a template for finer-grained risk mapping of seabed infrastructure networks in European seas and beyond.},
booktitle = {Computer {Safety}, {Reliability}, and {Security} ({SAFECOMP})},
author = {Franken, Jonas and Hagemeier, Martin and Dörnfeld, Timon and Löffler, Julian and Meissner, Paula and Reuter, Christian},
editor = {Troubitsyna, Elena and de Andrés, David and Bitsch, Friedemann},
year = {2026},
keywords = {Student, Security, Ranking-CORE-B, ATHENE-SecFOCI},
}

@article{frankenReadingCurrentTracking2026,
title = {Reading the {Current}: {Tracking} {Subsea} {Cable} {Status} through {LLM}-{Assisted} {News} {Analysis}},
abstract = {Subsea Data Cables (SDCs) form the backbone of global digital infrastructure, handling over 99\% of intercontinental data traffic. Still, they receive limited and dispersed media coverage, creating significant information gaps regarding their operational status. This paper aims to investigate the potential of Large Language Models (LLMs) in automating the extraction and analysis of SDCs-related information from unstructured media sources. A comprehensive LLM-based information extraction module was developed and integrated into an existing SDC database. By systematically comparing different LLMs, including GPT-4o, Gemini 1.5 Flash, Claude 3.5 Sonnet, and Llama 3.1, this work identifies optimal model configurations for SDC news processing, considering accuracy, hallucination rate, and processing speed. The system implements Claude 3.5 Sonnet and GPT4-o as primary models, incorporating domain-specific prompt engineering and robust output validation mechanisms. A performance evaluation demonstrates substantial improvements over rule-based methods. While excelling at natural language processing tasks, the system revealed limitations in extracting more specific technical details such as construction costs and capacity measurements. The implementation provides a modular, adaptable framework for automated information extraction in specialised technical domains, including maritime ones. The results demonstrate that LLMs, when properly implemented with structured prompts and validation mechanisms, can significantly enhance the automated monitoring and analysis of SDC-related events.},
journal = {Transactions on Maritime Science},
author = {Franken, Jonas and Romer, Kasimir and Dörnfeld, Timon and Meissner, Paula and Reuter, Christian},
year = {2026},
keywords = {Projekt-ATHENE-SecFOCI, Ranking-ImpactFactor, Security, Student},
}

@article{frankenHiddenStructuresGlobal2025,
title = {Hidden structures of a global infrastructure: {Expansion} factors of the subsea data cable network},
volume = {215},
issn = {0040-1625},
shorttitle = {Hidden structures of a global infrastructure},
url = {https://www.peasec.de/paper/2025/2025_FrankenReinholdDörnfeldReuter_TechForecasting.pdf},
doi = {10.1016/j.techfore.2025.124068},
abstract = {The network of subsea data cables (SDC) transmits the majority of international and intercontinental data exchanges. After thirty years of fiber-optic SDC installation across the oceans, almost all coastal and island countries gained access to the only global fixed infrastructure network. Still, there is considerable inequality in the number of available SDC accesses, creating deficits in redundancy for less connected states. Previous research hypothesized multiple factors that influenced the build-up of internet infrastructures but failed to verify these assumptions through inferential statistics. This work highlights the national-level factors that made backbone access provision more – or less – attractive to SDC project decision-makers. Our regression analysis of global country-year data (n = 4916) found that socio-economic (population, GDP), political (state fragility, conflict), and geographic factors (seismic hazard, neighboring territories) significantly influenced the number of active and planned accesses. This work can serve as a foundation for further research leveraging quantitative statistics to unveil hidden structures in the construction of material internet infrastructures and support sustainability in the future allocation of international infrastructure development resources in general.},
urldate = {2025-03-03},
journal = {Technological Forecasting and Social Change (TFSC)},
author = {Franken, Jonas and Reinhold, Thomas and Dörnfeld, Timon and Reuter, Christian},
month = jun,
year = {2025},
keywords = {Peace, A-Paper, AuswahlPeace, Security, Selected, Projekt-ATHENE-SecFOCI},
pages = {124068},
}

@inproceedings{frankenLinesLeveragingLLMs2025,
address = {Rostock, Germany},
title = {Between the {Lines}: {Leveraging} {LLMs} for {Information} {Extraction} of {Subsea} {Data} {Cable} {News}},
url = {https://zenodo.org/records/17119936},
doi = {10.5281/zenodo.17119935},
abstract = {This paper investigates the potential of Large Language Models (LLMs) in automating the extraction and analysis of Subsea Data Cables (SDCs)-related information from unstructured media sources. A comprehensive LLM-based information extraction module was developed and integrated into an existing SDC database. By systematically comparing different LLMs, including GPT-4o, Gemini 1.5 Flash, Claude 3.5 Sonnet, and Llama 3.1, this work identifies optimal model configurations for SDC news processing, considering accuracy, hallucination rate, and processing speed. The system implements Claude 3.5 Sonnet and GPT4-o as primary models, incorporating domain-specific prompt engineering and robust output validation mechanisms. Performance evaluation demonstrates substantial improvements over rule-based methods. While excelling at natural language understanding tasks, the system revealed limitations in extracting more specific technical details such as construction costs and capacity measurements. The implementation provides a modular, adaptable framework for automated information extraction in specialised technical domains. The results demonstrate that LLMs, when properly implemented with structured prompts and validation mechanisms, can significantly enhance the automated monitoring and analysis of SDC-related events.},
booktitle = {Proceedings of the {European} {Workshop} on {Maritime} {Systems}, {Resilience} and {Security} ({MARESEC})},
author = {Franken, Jonas and Romer, Kasimir and Dörnfeld, Timon and Meissner, Paula and Reuter, Christian},
year = {2025},
keywords = {Projekt-emergenCITY, Student, Security, emergenCITY\_INF, emergenCITY\_SG, Projekt-ATHENE-SecFOCI},
}