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SynopsisA honeypot is a security tool deliberately designed to be vulnerable, thereby enticing attackers to probe, exploit, and compromise it. Since their introduction in the early 1990s, honeypots have remained among the most widely used tools for capturing cyberattacks, complementing traditional defenses such as firewalls and intrusion detection systems. They serve both as early warning systems and as sources of valuable attack data, enabling security professionals to study the techniques and behaviors of threat actors.While conventional honeypots have achieved significant success, they remain deterministic in their responses to attacks. This is where adaptive or intelligent honeypots come into play. An adaptive honeypot leverages Machine Learning techniques, such as Reinforcement Learning, to interact with attackers. These systems learn to take actions that can disrupt the normal execution flow of an attack, potentially forcing attackers to alter their techniques. As a result, attackers must find alternative routes or tools to achieve their objectives, ultimately leading to the collection of more attack data.Despite their advantages, traditional honeypots face two main challenges. First, emulation-based honeypots (also known as low- and medium-interaction honeypots) are increasingly susceptible to detection, which undermines their effectiveness in collecting meaningful attack data. Second, real-system-based honeypots (also known as high-interaction honeypots) pose security risks to the hosting organization if not properly isolated and protected. Since adaptive honeypots rely on the same underlying systems, they also inherit these challenges.This thesis investigates whether it is possible to design a honeypot system that mitigates these challenges while still fulfilling its primary objective of collecting attack data. To this end, it proposes a new abstract model for adaptive self-guarded honeypots, designed to balance attack data collection, detection evasion, and security preservation, ensuring that it does not pose a risk to the rest of the network.Jury membersProf. Wim VANHOOF, President, University of NamurProf. Jean-Noël COLIN, Promoter, University of NamurProf. Florentin ROCHET, Internal Member, University of NamurProf. Benoît FRENAY, Internal Member, University of NamurProf. Ramin SADRE, External Member, Catholic University of LeuvenDr. Jérôme FRANCOIS, External Member, University of LuxembourgYou are cordially invited to a drink, which will follow the public defense. For good organization, please give your answer by Tuesday, May 20, 2025.

Synopsis deep learning methods have become increasingly popular for building intelligent systems. Currently, many deep learning architectures constitute the state of the art in their respective domains, such as image recognition, text generation, speech recognition, etc. The availability of mature libraries and frameworks to develop such systems is also a key factor in this success.This work explores the use of these architectures to build intelligent systems for sign languages. The creation of large sign language data corpora has made it possible to train deep learning architectures from scratch. The contributions presented in this work cover all aspects of the development of an intelligent system based on deep learning. A first contribution is the creation of a database for the Langue des Signes de Belgique Francophone (LSFB). This is derived from an existing corpus and has been adapted to the needs of deep learning methods. The possibility of using crowdsourcing methods to collect more data is also explored.The second contribution is the development or adaptation of architectures for automatic sign language recognition. The use of contrastive methods to learn better representations is explored, and the transferability of these representations to other sign languages is assessed.Finally, the last contribution is the integration of models into software for the general public. This led to a reflection on the challenges of integrating an intelligent module into the software development life cycle.Jury membersProf. Wim VANHOOF, President, University of NamurProf. Benoît FRENAY, Promoter, University of NamurProf. Anthony CLEVE, Co-promoter, University of NamurProf. Laurence MEURANT, Internal Member, University of NamurProf. Lorenzo BARALDI, External Member, University of ModenaProf. Annelies BRAFFORT, External Member, University of Paris-SaclayProf. Joni DAMBRE, External Member, University of GhentYou are cordially invited to a drink, which will follow the public defense. For a good organization, please give your answer by Friday June 6.

A delegation from the Université de Namur took part in an exploratory mission to the Université Cheikh Anta Diop (UCAD) in Dakar, Senegal. The aim: to discover the research carried out in the field, meet UCAD researchers and initiate future collaborations between the two institutions.

On December 11, 2024, the University of Reims-Champagne-Ardenne hosted the launch event for the ORION project, in which the University of Namur is a partner. This project, financed for 4 years by ERDF and INTERREG funds, aims to improve water management in the Val de Meuse while preserving the ecosystems of the Val de Meuse, a river running through France and Belgium.

Max Collinet, professor of geology at the Faculty of Science and researcher at the Institute of Life, Earth and Environment (ILEE), has just been awarded equipment funding (EQP) from the F.R.S - FNRS following calls whose results were published in December 2024.

From March 31 to April 4, 2025, a Belgian delegation, including UNamur Rector Annick Castiaux, took part in a royal mission to Vietnam aimed at strengthening economic, academic and scientific ties between the two countries. The mission was punctuated by several milestone events highlighting UNamur's fruitful collaborations with Vietnamese institutions.

The program Interventions by experts and researchers in the field of data science, , AI, digital twins, digital law and participatory processes.Registrations on the Vivre la Ville... website.

Founded some 50 years ago, the Laboratoire d'Analyse par Réactions Nucléaires (LARN) in the Department of Physics at the University of Namur is home to a 2MV tandem particle gas pedal named ALTAÏS (Accélérateur Linéaire Tandetron pour l'Analyse et l'Implantation des Solides), in operation since 1999.

His long-term collaboration with Dr. Aniruddha Chatterjee recently resulted in the first collaborative framework agreement between the University of Otago and UNamur, as well as an international Erasmus+ (EU) credit mobility funding this scientific mission. Frédéric Silvestre deepened his understanding of the advanced DNA methylation techniques employed by Dr. Chatterjee's team, including their bioinformatics workflows and innovative approach to epigenetic editing. He has given several seminars presenting LEAP's research on mangrove rivulus and turquoise molefish. He also presented at the Australasian Epigenetic Alliance conference and had the opportunity to explore potential new collaborations with other departments, including zoology, sustainable development, chemistry and marine sciences.

Quentin Willot (Biology) has just joined URBE for a two-year post-doctoral position (Marie Sklodowska-Curie Action, supervisor: Alice Dennis) on a project called ChillAnts. This project focuses on the study of adaptation to extreme temperatures in holarctic (trans-Beringian) ant species, from a physiological, ecological and evolutionary point of view. Quentin will present a more technical aspect of his work (Thermal Death Time Curve Modelling).

Space has become a major economic and strategic issue. As a member of the European UNIVERSEH Alliance, UNamur explores this space theme in its various departments, from physics to geology, via mathematics, computer science or philosophy. Without forgetting to address the general public, who still dream of the stars...

Can a computer learn a language like a child? A recent study published in the leading journal Computational Linguistics by Professors Katrien Beuls (Université de Namur) and Paul Van Eecke (AI-lab, Vrije Universiteit Brussel) sheds new light on this question. The researchers argue for a fundamental revision of the way artificial intelligence acquires and processes language.