Arnaud-Fassetta et al. (2026) When geomorphological field data and systemic analysis help refine the uncertainties of numerical hydrometeorological models in extreme values. Case study: The catastrophic flood event of October 14-15, 2018, in the Aude watershed (southern France)

Identification

• Journal: Géomorphologie relief processus environnement
• Year: 2026
• Date: 2026-01-01
• Authors: Gilles Arnaud-Fassetta, Mathieu Brun, Mathieu Dupuis, Typhaine Bellon, Léa Perrine, Guillaume Brousse, Monique Fort
• DOI: 10.4000/15rpd

Research Groups

• Techaro (Developer of Anubis)
• CELPHASE (Mascot design)

Short Summary

Anubis is a server-side bot detection and protection system that employs a Proof-of-Work (PoW) scheme, inspired by Hashcash, to deter aggressive AI scraping by increasing the computational cost for mass scrapers while aiming to minimize impact on legitimate users.

Objective

• To protect web servers from aggressive AI scraping that causes service downtime.
• To implement a Proof-of-Work challenge to make mass data scraping economically unfeasible for AI companies.
• To serve as an interim solution while more advanced headless browser fingerprinting and no-JavaScript challenge methods are developed.

Study Configuration

• Spatial Scale: Server-side implementation, protecting individual websites globally where Anubis is deployed.
• Temporal Scale: Real-time challenge presentation upon suspected bot detection; continuous development and updates (e.g., Anubis version 1.25.0 mentioned).

Methodology and Data

• Models used: Proof-of-Work (PoW) scheme, conceptually similar to Hashcash, requiring client-side computation.
• Data sources: Client-side JavaScript execution for PoW completion; future plans include browser fingerprinting data (e.g., font rendering characteristics) to identify headless browsers.

Main Results

• Anubis successfully presents a JavaScript-based Proof-of-Work challenge to users suspected of being bots.
• It aims to increase the operational cost for mass scrapers, thereby reducing their impact on server resources.
• The system currently requires modern JavaScript features, which may necessitate disabling certain privacy plugins (e.g., JShelter) for legitimate users.

Contributions

• Offers a practical, albeit temporary, compromise solution for website administrators facing aggressive AI scraping.
• Implements a Proof-of-Work mechanism as a deterrent, shifting computational burden to potential scrapers.
• Highlights the evolving challenge of bot detection and outlines future development directions, including more sophisticated fingerprinting and non-JavaScript solutions.

Funding

• Not explicitly stated in the provided text, but implied to be internal development by Techaro.

Citation

@article{ArnaudFassetta2026When,
  author = {Arnaud-Fassetta, Gilles and Brun, Mathieu and Dupuis, Mathieu and Bellon, Typhaine and Perrine, Léa and Brousse, Guillaume and Fort, Monique},
  title = {When geomorphological field data and systemic analysis help refine the uncertainties of numerical hydrometeorological models in extreme values. Case study: The catastrophic flood event of October 14-15, 2018, in the Aude watershed (southern France)},
  journal = {Géomorphologie relief processus environnement},
  year = {2026},
  doi = {10.4000/15rpd},
  url = {https://doi.org/10.4000/15rpd}
}