Repository logo
 

From playing games to committing crimes: A multi-technique approach to predicting key actors on an online gaming forum

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Hughes, Jack 
Collier, Ben 
IEEE 

Abstract

We propose a systematic framework for analysing forum datasets, which contain minimal structure, and are nontrivial to analyse at scale, aiming to support future analysis of underground forum communities. We use a multi-technique approach which draws on a combination of features, including post classifications extracted using natural language processing tools, and apply clustering and predictive techniques to this dataset, to predict potential key actors-individuals who have a central role in overtly criminal activities, or activities which could lead to later offending, and hence might benefit most from interventions. We predict 49 key actors on an underground gaming-specific cheating and hacking forum, validated by observing only overlaps of techniques, combined with topic analysis, to build a classifier for key actor status. In addition, we also use these techniques to provide further insight of key actor activity. We found one cluster and two posting trajectories to contain a high proportion of key actors, logistic regression found an actor's h-index to have higher odds for prediction than other features, and partial dependence plots found reputation to have a significant change in prediction between values of 100 to 1000.

Description

Keywords

Cybercrime, Underground Forums, Online Gaming, Pathways, Key Actors

Journal Title

2019 APWG SYMPOSIUM ON ELECTRONIC CRIME RESEARCH (ECRIME)

Conference Name

2019 APWG Symposium on Electronic Crime Research (eCrime)

Journal ISSN

2159-1237
2159-1245

Volume Title

Publisher

IEEE
Sponsorship
Engineering and Physical Sciences Research Council (EP/M020320/1)
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/M020320/1