Title
Template Clustering for the Foundational Analysis of the Dark Web
Author
Nair, V.V.
van Staalduinen, M.
Oosterman, D.T.
Publication year
2021
Abstract
The rapid rise of the Dark Web and supportive technologies has served as the backbone facilitating online illegal activity worldwide. These illegal activities supported by anonymisation technologies such as Tor has made it increasingly elusive to law enforcement agencies. Despite several successful law enforcement operations, illegal activity on the Dark Web is still growing. There are approaches to monitor, mine, and research the Dark Web, all with varying degrees of success. Given the complexity and dynamics of the services offered, we recognize the need for in depth analysis of the Dark Web with regard to its infrastructures, actors, types of abuse and their relationships. This involves the challenging task of information extraction from the very heterogeneous collection of web pages that make up the Dark Web. Most providers develop their services on top of standard frameworks such as WordPress, Simple Machine Forum, phpBB and several other frameworks to deploy their services. As a result, these service providers publish significant number of pages based on similar structural and stylistic templates. We propose an efficient, scalable, repeatable and accurate approach to cluster Dark Web pages based on those structural and stylistic features. Extracting relevant information from those clusters should make it feasible to conduct in depth Dark Web analysis. This paper presents our clustering algorithm to accelerate information extraction, and as a result improve attribution of digital traces to infrastructures or individuals in the fight against cyber crime.
Subject
Artificial Intelligence
Cybercrime
Machine Learning
Template Clustering
Web Mining
Clustering algorithms
Computer crime
Crime
Cybersecurity
Data mining
Information retrieval
Machine learning
Network security
Clusterings
Cyber-crimes
Dark web
Illegal activities
Machine-learning
Supportive technologies
Template clustering
Web technologies
Web-page
Websites
To reference this document use:
http://resolver.tudelft.nl/uuid:81639d7b-7b9b-4643-9a52-c5f5b0b3a12b
TNO identifier
966033
Publisher
Institute of Electrical and Electronics Engineers Inc.
ISBN
9781665439022
ISSN
0000-0000
Source
Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021, 2021 IEEE International Conference on Big Data, Big Data 2021, 15 December 2021 through 18 December 2021, 2542-2549
Bibliographical note
Sponsor: Ankura Collaboration Drives Results;IEEE;IEEE Computer Society;Lyve Cloud;NSF;Seagate
Document type
conference paper