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.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Detecting Criminal Networks via Non-content Communication Data Analysis Techniques from the TRACY Project

  • Pradeep Rangappa,
  • Amanda Muscat,
  • Alejandra Sanchez Lara,
  • Petr Motlicek,
  • Michaela Antonopoulou,
  • Ioannis Fourfouris,
  • Antonios Skarlatos,
  • Nikos Avgerinos,
  • Manolis Tsangaris,
  • Kasia Kostka

摘要

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.