APAMCiber

Prevention and Attribution of Cyberthreats against Cybersecurity Stakeholders

Duration: September 2023 - December 2025

Researchers

Abstract

This project revolves around three main areas. On the one hand, it aims to propose an observatory of tools and technologies that, either by design or by the use made of them, pose a risk to vulnerable groups. On the other hand, to focus on the “average user” of the internet systems and its associated risks. This user not only does not have technical knowledge high, but also runs certain risks intentionally or by ignorance. Lastly, improving the understanding of the problems of security by the creators of the systems themselves (or possibly by the security analysts).

Publications

Eichler, Cédric; Champeil, Nathan; Anciaux, Nicolas; Bensamoun, Alexandra; de Fuentes, José María; Arcolezi, Héber. Nob-MIAs: Non-biased Membership Inference Attacks Assessment on Large Language Models with Ex-Post Dataset Construction. Proceedings WISE 2024. WISE.

Brahem, Mariem; Watissee, Jasmine; Eichler, Cédric; Boiret, Adrien; Anciaux, Nicolas; de Fuentes, José María. reteLLMe: Design rules for using Large Language Models to Protect the Privacy of Individuals in their Textual Contributions. Proceedings Data Privacy Management (DPM), co-located with ESORICS. Springer.

de la Cruz, Alejandro; Pastrana, Sergio. Understanding Crypter-as-a-service in a popular underground market. Workshop on Attackers and Cyber-Crime Operations (WACCO). Springer.

Ibanez-Lissen, Luis; de Fuentes, José María; González-Manzano, Lorena; Anciaux, Nicolas. Continuous Authentication Leveraging Matrix Profile. 19th International Conference on Availability, Reliability and Security (ARES 2024). Springer.

Ibanez-Lissen, Luis; González-Manzano, Lorena; de Fuentes, José María; Goyanes, Manuel. Poster: On the feasability of predicting fake news appearance. Jornadas Nacionales de Investigación en Ciberseguridad (JNIC 2024). JNIC.

Ibanez-Lissen, Luis; de Fuentes, José María; González-Manzano, Lorena; Goyanes, Manuel. Use of transfer learning for affordable in-context fake review generation. IEEE Transactions of Big Data.

This project has received funding from the European Union (Next Generation), Instituto Nacional de Ciberseguridad under the project APAMCiber, Secretaría de Estado de Digitalización e Inteligencia Artificial and the Recovery, Transformation and Resilience Plan.
Published on Thursday, Jul 20, 2023 Last Modified on Thursday, Dec 12, 2024