Detail publikačního výsledku

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.

Originální název

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

Anglický název

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

Druh

Stať ve sborníku mimo WoS a Scopus

Originální abstrakt

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.

Anglický abstrakt

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.

Klíčová slova

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

Klíčová slova v angličtině

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

Autoři

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

Vydáno

09.10.2024

Místo

Dubrovnik

Kniha

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

Strany od

1

Strany do

15

Strany počet

15

URL

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

Plný text v Digitální knihovně

http://hdl.handle.net/

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"
}

Dokumenty

rangappa_EAIICDF2C2024_2024