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A Methodology For Large-Scale Identification of Related Accounts in Underground Forums
یک روش برای شناسایی در مقیاس بزرگ حساب های مرتبط در انجمن های زیرزمینی-2021 Underground forums allow users to interact with communities focused on illicit activities.
They serve as an entry point for actors interested in deviant and criminal topics. Due to the
pseudo-anonymity provided, they have become improvised marketplaces for trading illegal
products and services, including those used to conduct cyberattacks. Thus, these forums
are an important data source for threat intelligence analysts and law enforcement. The use
of multiple accounts is forbidden in most forums since these are mostly used for malicious
purposes. Still, this is a common practice. Being able to identify an actor or gang behind
multiple accounts allows for proper attribution in online investigations, and also to design
intervention mechanisms for illegal activities. Existing solutions for multi-account detec-
tion either require ground truth data to conduct supervised classification or use manual
approaches. In this work, we propose a methodology for the large-scale identification of re-
lated accounts in underground forums. These accounts are similar according to the distinc-
tive content posted, and thus are likely to belong to the same actor or group. The methodol-
ogy applies to various domains and leverages distinctive artefacts and personal information
left online by the users. We provide experimental results on a large dataset comprising more
than 1.1M user accounts from 15 different forums. We show how this methodology, com-
bined with existing approaches commonly used in social media forensics, can assist with
and improve online investigations.
© 2021 Elsevier Ltd. All rights reserved. keywords: رسانه های اجتماعی قانونی | انجمن های زیرزمینی | اندازه گیری در مقیاس بزرگ | حساب های مرتبط | سایبری | Social media forensics | Underground forums | Large-Scale measurement | Related accounts | Cybercrime |
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