Author ORCID Identifier
Document Type
Article
Disciplines
Computer Sciences
Abstract
Abstract: Child sexual abuse material (CSAM) activities are prevalent on the Dark Web to evade detection, posing a global challenge for law enforcement. Our objective is to analyze CSAM discussions in this concealed space using a Support Vector Machine model, achieving an accuracy of 87.6%. Across eight forums, approximately 28.4% of posts contained CSAM, with victim ages most commonly reported as 12, 14, 13, and 11 years old for YouTube, Skype, Instagram, and Facebook, respectively. Additionally, in forums discussing boys, the most frequently mentioned nationalities in CSAM posts were English, German, and American, accounting for 12%, 7.8%, and 6% of all nationalities, respectively.
DOI
https://doi.org/10.21427/JVDD-BS95
Recommended Citation
2023, Vuong M. Ngo, Christina Thorpe, Susan Mckeever, Analysing Child Sexual Abuse Activities in the Dark Web based on an Efficient CSAM Detection Algorithm, The second annual Trust and Safety Research Conference. Stanford University, September 28-29, 2023. DOI: 10.21427/JVDD-BS95
Funder
Tech Coalition Safe Online Research Fund
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Publication Details
The second annual Trust and Safety Research Conference Stanford University, California, USA September 28-29, 2023