cmp81/cyberattack-blockchain-synth

ELTEX-Blockchain: A Domain-Specific Dataset for Cybersecurity

🔐 12k Synthetic Social Media Messages for Early Cyberattack Detection on Blockchain

Dataset Statistics

Category	Samples	Description
Cyberattack	6,941	Early warning signals and indicators of cyberattacks
General	4,507	Regular blockchain discussions (non-security related)

Dataset Structure

Each entry in the dataset contains:

• message_id: Unique identifier for each message
• content: The text content of the social media message
• topic: Classification label ("cyberattack" or "general")

Performance

Gemma-2b-it fine-tuned on this dataset:

• Achieves a Brier score of 0.16 using only synthetic data in our social media threat detection task
• Shows competitive performance on this specific task when compared to general-purpose models known for capabilities in cybersecurity tasks, like granite-3.2-2b-instruct, and cybersecurity-focused LLMs trained on Primus
• Demonstrates promising results for smaller models on this specific task, with our best hybrid model achieving an F1 score of 0.81 on our blockchain threat detection test set, though GPT-4o maintains superior overall accuracy (0.84) and calibration (Brier 0.10)

Attack Type Distribution

Attack Vectors	Seed Examples
Social Engineering & Phishing	Credential theft, wallet phishing
Smart Contract Exploits	Token claim vulnerabilities, flash loans
Exchange Security Breaches	Hot wallet compromises, key theft
DeFi Protocol Attacks	Liquidity pool manipulation, bridge exploits

For more about Cyberattack Vectors, read Attack Vectors Wiki

Citation

@misc{razmyslovich2025eltexframeworkdomaindrivensynthetic,
      title={ELTEX: A Framework for Domain-Driven Synthetic Data Generation}, 
      author={Arina Razmyslovich and Kseniia Murasheva and Sofia Sedlova and Julien Capitaine and Eugene Dmitriev},
      year={2025},
      eprint={2503.15055},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2503.15055}, 
}