Publication result detail

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

RANGAPPA, P.; MUSCAT, A.; SANCHEZ-LARA, A.; MOTLÍČEK, P.; ANTONOPOULOU, M.; FOURFOURIS, I.; SKARLATOS, A.; AVGERINOS, N.; TSANGARIS, M.; KOSTKA, K.

Original Title

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

English Title

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

Type

Paper in proceedings outside WoS and Scopus

Original Abstract

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.

English abstract

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.

Keywords

Law Enforcement Agencies, Suspect DetectionNon-Content Data, Social Influence Analysis, Link Prediction

Key words in English

Law Enforcement Agencies, Suspect DetectionNon-Content Data, Social Influence Analysis, Link Prediction

Authors

RANGAPPA, P.; MUSCAT, A.; SANCHEZ-LARA, A.; MOTLÍČEK, P.; ANTONOPOULOU, M.; FOURFOURIS, I.; SKARLATOS, A.; AVGERINOS, N.; TSANGARIS, M.; KOSTKA, K.

Released

09.10.2024

Location

Dubrovnik

Book

Proceedings of the15th EAI International Conference on Digital Forensics & Cyber Crime (EAI-ICDF2C24)

Pages from

1

Pages to

15

Pages count

15

URL

https://publications.idiap.ch/attachments/papers/2024/Rangappa_EAIICDF2C2024_2024.pdf

BibTex

@inproceedings{BUT196783,
  author="RANGAPPA, P. and MUSCAT, A. and SANCHEZ-LARA, A. and MOTLÍČEK, P. and ANTONOPOULOU, M. and FOURFOURIS, I. and SKARLATOS, A. and AVGERINOS, N. and TSANGARIS, M. and KOSTKA, K.",
  title="Detecting Criminal Networks via Non-Content Communication Data Analysis Techniques from the TRACY Project",
  booktitle="Proceedings of the15th EAI International Conference on Digital Forensics & Cyber Crime (EAI-ICDF2C24)",
  year="2024",
  pages="1--15",
  address="Dubrovnik",
  url="https://publications.idiap.ch/attachments/papers/2024/Rangappa_EAIICDF2C2024_2024.pdf"
}

Documents

rangappa_EAIICDF2C2024_2024