CONF Rangappa_EAIICDF2C2024_2024/IDIAP Detecting Criminal Networks via Non-Content Communication Data Analysis Techniques from the TRACY Project Rangappa, Pradeep Amand, Muscat Lara, Alejandra Sanchez Motlicek, Petr Antonopoulou, Michaela Fourfouris, Ioannis Skarlatos, Antonios Avgerinos, Nikos Tsangaris, Manolis Kostka, Kasia TRACY · Law Enforcement Agencies · Suspect Detection· Non-Content Data· Social Influence Analysis· Link Prediction EXTERNAL http://publications.idiap.ch/attachments/papers/2024/Rangappa_EAIICDF2C2024_2024.pdf PUBLIC Digital Forensics and Cyber Crime. ICDF2C 2024 Dubrovnik, Croatia Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 613 1867-8211 978-3-031-89363-6 2024 https://link.springer.com/book/9783031893629 URL https://doi.org/10.1007/978-3-031-89363-6_20 doi This paper explores the critical role of non-content data (NCD), provided by electronic communications service providers in aiding criminal investigations. As highlighted by the Law Enforcement Agencies (LEAs) and the European Commission, NCD plays a fundamental role in identifying suspects and discerning behavioral patterns. Despite its significance, LEAs encounter various challenges in effectively analyzing the extensive volume of NCD. To address this issue, this paper presents the importance of (although simulated but realistic) data collection, the technologies that can be built and the methods for detecting the suspect within the framework of the TRACY project. These techniques aim to enhance capabilities of LEAs by processing large-scale NCD and aligning it with existing evidence. By prioritizing the tracing of suspects movements and integrating data from diverse NCD sources, TRACY’s initial approach on synthetic data promises to significantly advance the identification of offenders involved in serious and organized crime.