2026.05.18Paper presented at IEEE/IFIP NOMS 2026 (MCT workshop) on a graph-based approach (GPML) for detecting cyberattacks in water distribution networks from topology changes.
Paper presented at EvoApplications 2026 (Toulouse) on an evolutionary-ML approach building lightweight probabilistic surrogates for cyber-attack detection on edge devices in Water Distribution Networks.
Julien Michel defended his PhD thesis, 'Real-time robust attack detection using graph community metrics', on 8 April 2026 at the University of Strasbourg.
Majed Jaber defended his PhD thesis, 'Structural and spectral analysis of dynamic graphs for attack detection', on 19 December 2025 at the University of Strasbourg.
Paper presented at KES 2025 (Osaka) defining t-robustness and a feature engineering approach, evaluated on UGR16 enriched with graph community metrics, that produces attack-detection models more stable under concept drift.
2025.09.01Journal article (SoftwareX) presenting GPML, an open-source library that transforms raw network traffic into graph representations for community and spectral-metric-based cyberattack detection.
2024.07.30Paper presented at ARES 2024 (Vienna) introducing SpectraTW, a spectral graph analysis methodology using four spectral indicators to detect cyberattacks from network topology, with a focus on medical IoT.
2024.07.01Paper presented at WDSA/CCWI 2024 (Ferrara) providing a multi-layer machine-learning approach that combines physical and network traffic data, assessing its effect on attack-detection performance and its cross-impact with graph-metric-enriched data.
2023.01.01XDGMed develops explainable graph-based methods for detecting attacks in medical and other sensitive dynamic systems, combining graph analysis with trusted artificial intelligence principles.
DAMIAGE develops active, graph-based attack detection for large-scale critical infrastructures, combining network traffic with operational signals to support real-time or near-real-time cybersecurity.