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  • Summary
  • Contribution
  • Data
  • Model
  • Empirical Study
  1. Security
  2. IllicitPromotion

DarkGram: A Large-Scale Analysis of Cybercriminal Activity Channels on

Link: https://arxiv.org/pdf/2409.14596

Conference: Usenix Security 25

Keywords: Cybercrime, telegram, empirical study

Open source: https://github.com/UTA-SPRLab/DarkGram

Summary

Develop DarkGram, a BERT-based framework to identify malicious posts from cybercrimial activity channels (CACs). Conduct empirical study on 339 CACs followed by over 23.8M users.

Contribution

  1. Monitoered 339 Telegram channels followed by 23.8M users associated with 5 categories of cybercrimal content: 1) Compromised user credentials, 2) Pirated software, 3) Pirated media, 4) Social media manipulation tools, and 5) Blackhat hacking resources\

  2. Developed DarkGram, a BERT-based framework that automatically detects malicious posts with an average accuracy of 96% across the five categories.

  3. Empirical study on user engagement with posts; CAC malicious behaviour to subscribers (The underworld fights against the underworld)

Data

  1. Find popular channels with Telemetr.io. Maunally check and find out 339 channels out of 4,709 channels have features in Federal Bureau of Investigation’s Internet Crime Report for 10 most recent posts.

  2. Using Telegram API collecting 64,801 posts between 21 Feb to 29 May 2024.

  3. Label: random select 10 posts from each channel labeled from 2 coders and cohen's kappa is 0.78 (substantial agreement and the disagreements were resolved, a score greater than 0.75 is considered excellent)

Model

BERT-based, 2 classifers: Cybercriminal Activity (CA) post or not, and detailed cybercrimal categories.

Empirical Study

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