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Information Extraction from Darknet Market Advertisements and Forums

Authors:
Sven Schlarb
Clemens Heistracher
Faisal Ghaffar

Keywords: natural language processing; Information extraction; named entity recognition; relationship extraction, event detection

Abstract:
Over the past decade, the Darknet has created unprecedented opportunities for trafficking in illicit goods, such as weapons and drugs, and it has provided new ways to offer crime as a service. Along with the possibilities of concealing financial transactions with the help of crypto currencies, the Darknet offers sellers the possibility to operate in covert. This article presents research and development outcomes of the COPKIT project which are relevant to the SECURWARE 2020 conference topics of data mining and knowledge discovery from a security perspective. It gives an overview about the methods, technologies and approaches chosen in the COPKIT project for building information extraction components with a focus on Darknet Markets. It explains the methods used to gain structured information in form of named entities, the relations between them, and events from unstructured text data contained in Darknet Market web pages.

Pages: 34 to 39

Copyright: Copyright (c) IARIA, 2020

Publication date: November 21, 2020

Published in: conference

ISSN: 2162-2116

ISBN: 978-1-61208-821-1

Location: Valencia, Spain

Dates: from November 21, 2020 to November 25, 2020