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PostCog: A tool for interdisciplinary research into underground forums at scale

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Pete, I 
Caines, A 
Vu, AV 
Gupta, H 

Abstract

Underground forums provide useful insights into cybercrime, where researchers analyse underlying economies, key actors, their discussions and interactions, as well as different types of cybercrime. This interdisciplinary topic of study incorporates expertise from diverse areas, including computer science, criminology, economics, psychology, and other social sciences. Historically, there were significant challenges around access to data, but there are now research datasets of millions of messages scraped from underground forums. The problems now stem from the size of these datasets and the technical nature of methods and tools available for data sampling and analysis at scale, which make data exploration difficult for non-technical users. POSTCOG has been developed to solve this problem. We first provide a survey of prior work into underground forums; this was used to understand the requirements and functionalities valued by researchers, and to inform the design of a data exploration tool. We then describe POSTCOG, a web application developed to support users from both technical and non-technical backgrounds in forum analyses, such as search, information extraction and cross-forum comparison. The prototype’s usability is then evalu- ated through two user studies with expert users of the CRIMEBB dataset. POSTCOG is made available for academic research upon signing an agreement with the Cambridge Cybercrime Centre.

Description

Keywords

cybercrime, forums, interdisciplinary research tools, data analysis

Journal Title

Proceedings - 7th IEEE European Symposium on Security and Privacy Workshops, Euro S and PW 2022

Conference Name

2022 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW)

Journal ISSN

Volume Title

Publisher

IEEE
Sponsorship
ESRC (ES/T008466/1)
European Commission Horizon 2020 (H2020) ERC (949127)
This work was supported by the Economic and Social Research Council (ESRC) (grant number ES/T008466/1) and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 949127).