Reliable Distributed Systems

41

employees

45

publications

34

projects

The global push toward resilient digital infrastructure is transforming how interconnected systems operate. This evolution demands robust cybersecurity, where advanced machine learning helps detect and mitigate complex threats, ensuring resilience against evolving attacks. At the same time, the emergence of 6G and beyond is driving the need for scalable, adaptive connectivity that integrates communication, computation and AI to handle diverse, dynamic service demands. Effective networking and communication technologies are critical, providing the reliable, high-speed interactions needed for secure and efficient digital ecosystems. At the same time, optimization methods are essential for improving decision-making and operational efficiency, addressing the complex, distributed nature of modern networks, and supporting seamless, resilient digital operations.

Mission

The Reliable Distributed Systems unit aims to address these challenges by integrating advances in cybersecurity, networking and communication, operational research, and computing technologies. The unit builds on the latest advancements in technologies such as 6G, connectivity and networking, robust AI, digital twins and distributed ledgers to design innovative solutions that meet the demands of future digital ecosystems. The overarching aim is to develop real-world solutions grounded in reliable and robust approaches. 

The unit focuses on creating secure, autonomous, and adaptable systems capable of coordinating distributed resources and maintaining integrity, confidentiality and availability. This involves optimizing both the structural defenses and operational resilience of interconnected and networked digital systems. Connectivity measures ensure that networked systems operate seamlessly and securely under dynamic or hostile conditions, supporting advancements across both fundamental communication layers (e.g., protocols and infrastructure) and higher-level distributed intelligence for adaptive and scalable networks. Cybersecurity includes risk management, applied cryptography and privacy-enhancing technologies, fault-tolerance technologies such as consensus mechanisms in blockchain and distributed ledger technologies (DLT) applications, and mechanisms for detecting, mitigating, and preventing threats, including anomalies, adversarial intrusions, and data tampering across interconnected devices or nodes. Optimization efforts target efficient resource allocation and system performance, which is critical to meeting the demands of dynamic applications and interconnected systems.

Scope of expertise

Leveraging innovative machine learning techniques to address complex cybersecurity challenges, while ensuring system resilience against security risks to build a secure and trusted digital environment.

Integrating communication, computation and AI to design scalable and adaptive networks, with a focus on future 6G and beyond systems and their ability to meet evolving service demands and deployment complexities. 

Advancing communication technologies that ensure reliable and efficient interactions across diverse domains, paving the way for a safer, interconnected world.

Designing advanced optimization methods to improve decision-making processes and operational efficiency across industries, addressing complex and dynamic challenges.

Our latest projects

MOBICLUS

Studying mobility challenges in geographical zones and clusters in Luxembourg

SAFE-HIRE

Secure AI-Assisted Framework for Recruitment and LLM enhancement

AURELA-AI

Automated Risk Recognition and Policy Derivation from Legislative and Jurisdictional Documents via Large Language Models

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Our latest publications

Combining large language and deep learning models for media text classification to perform supply chain due diligence risk assessments

Guo Y., Krause J., Khadraoui D., Cortina S., Aeckerle-Willems C., Viroli F.

EPJ Data Science, vol. 15, n° 1, art. no. 16, 2026

Reconciling Urban Mobility and CCAM Digital Twins for Enhanced Integration and Mutual Advancement

Feltus C., Ferrero F., Nicolas D., Viti F., Castignani G., Connors R., Khadraoui D., Nakao H.

Data Science for Transportation, vol. 8, n° 1, art. no. 4, 2026

Leveraging the DAO for Edge-to-Cloud Data Sharing and Availability

Imeri A., Roth U., Kourtis M.A., Oikonomakis A., Economopoulos A., Fogli L., Cadeddu A., Bianchini A., Iglesias D., Tavernier W.

Future Internet, vol. 18, n° 1, art. no. 37, 2026

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