Omkar2118/datasets huggingface dataset:
Social Media Messages for Early Cyberattack Detection on Blockchain

# 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](https://huggingface.co/google/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](https://huggingface.co/ibm-granite/granite-3.2-2b-instruct), and cybersecurity-focused LLMs trained on [Primus](https://huggingface.co/papers/2502.11191)
- 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](https://dn.institute/research/cyberattacks/wiki/)

## Citation

```bibtex
@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}, 
}
```

Introducing paper: ELTEX: A Framework for Domain-Driven Synthetic Data Generation
Headgear Image Classification Dataset

Description:

<a href="https://gts.ai/dataset-download/headgear-image-classification-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset provides a comprehensive collection of headgear images classified into 20 distinct categories, including hats, caps, and helmets. It contains a total of 3,620 training images, 100 testing images, and 100 validation images. Each image is consistently sized at 224 x 224 pixels, with 3 color channels (RGB), and is saved in high-quality JPEG format. The dataset is designed to be a resource for image classification tasks, particularly in projects related to clothing, fashion accessories, or safety gear detection. The diversity of the headgear classes allows for applications in industries such as retail, e-commerce, safety compliance, and more.

Download Dataset

Dataset Details:

Image Resolution: All images are 224 x 224 pixels, RGB format (3 channels).

File Format: JPEG (.jpg) format for all images.

Classes: 20 categories of headgear, including various styles of hats, caps, and helmets.

• Training Data: 3,620 images.

Test Data: 100 images.

Validation Data: 100 images.

Additional Resources:

Alongside the image dataset, the package includes a pre-trained model using the EfficientNetBO architecture. This model has been fine-tuned for headgear classification, providing a strong baseline for further fine-tuning or deployment in production environments. The accompanying CSV file offers detailed annotations, including file names and their respective class labels, which can be utilize for both model training and evaluation.

Applications:

This dataset is ideal for machine learning practitioners and researchers working on computer vision tasks. It can be use for:

Headgear Recognition: Identifying and classifying different types of headgear from images.

Retail and E-Commerce Solutions: Enhancing search and recommendation engines by recognizing headgear in product images.

Safety and Compliance: Detecting helmets or other safety-relate gear in images for industries where protective equipment is require.

Fashion and Accessory Classification: Categorizing various styles of headgear in fashion-relate projects.

This dataset is sourced from Kaggle.
Wheat Plant Diseases Dataset

Description:

<a href="https://gts.ai/dataset-download/wheat-plant-diseases-dataset/" target="_blank">👉 Download the dataset here</a>

The Wheat Plant Diseases Dataset is a comprehensive collection of high-resolution images designed to assist researchers, agronomists, and developers in the development of advanced machine learning models for the classification and diagnosis of various wheat plant diseases. This dataset aims to contribute to the sustainable management of wheat crops by enabling the early detection and treatment of diseases, ultimately safeguarding food security.

Download Dataset

Dataset Content

Total Number of Images: 14,155

Image Quality: High-resolution images capturing real-world disease conditions, devoid of any artificial augmentations to preserve the authenticity and natural variability of the dataset.

Disease Classes: The dataset covers a wide range of wheat plant diseases, categorized into the following classes:

Pest-related Diseases:

Aphid: A common pest known to cause yellowing and stunted growth in wheat plants.

Mite: Tiny arachnids that feed on the plant sap, leading to discoloration and leaf curling.

Stem Fly: Insects that lay eggs in the stems of wheat plants, causing structural damage and reduced yield.

Fungal Diseases:

Rusts: A group of fungal diseases, each causing different symptoms but all leading to significant crop loss.

Black Rust / Stem Rust: Causes dark, elongated pustules on stems and leaves.

Brown Rust / Leaf Rust: Results in orange-brown pustules primarily on the leaves.

Yellow Rust / Stripe Rust: Characterized by yellow stripes running along the length of the leaves.

Benefits of the Wheat Plant Diseases Dataset

Extensive Coverage: With over 14,000 images, the dataset provides a robust foundation for developing machine learning models capable of identifying a wide range of wheat diseases.

Authenticity: The dataset contains real-world images, free from artificial augmentation, ensuring that the trained models are more likely to perform well in practical scenarios.

Educational Value: The inclusion of disease causes and visual monitoring guides makes this dataset not only a tool for machine learning but also an educational resource for understanding wheat plant health.

Enhanced Agricultural Practices: By utilizing this dataset, stakeholders in agriculture can adopt more proactive and informed approaches to disease management, leading to healthier crops and higher yields.

Conclusion

The Wheat Plant Diseases Dataset is an indispensable resource for anyone involved in agricultural research, disease diagnosis, and crop management. Its extensive and varied image collection, coupled with detailed disease information, makes it a powerful tool for advancing wheat disease detection through Al and machine learning.

This dataset is sourced from Kaggle.
Plant Disease Image Dataset

Description

<a href="https://gts.ai/dataset-download/plant-disease-image-dataset/" target="_blank">👉 Download the dataset here</a>

This dataset offers an extensive collection of images and corresponding labels representing a wide array of plant diseases. Carefully curated from publicly available sources, it serves as a valuable resource for developing and evaluating machine learning models, particularly in the realms of image classification and Plant Disease Image Dataset.

Dataset Composition:

• Images: The dataset comprises high-quality images organized by plant species and disease type, providing a diverse range of visual data. It includes not only images of diseased plants but also healthy plant samples, ensuring a balanced dataset for training purposes.

• Categories: The images are categorized into various classes based on the plant species and the specific disease affecting them. This categorization allows for more precise model 
training and testing.

• Cleaned Data: All images have been meticulously cleaned and verified to remove any corrupt or unusable files, ensuring the dataset's reliability and usability.

• Labeling: Each image is labeled with detailed information about the plant species and the type of disease, making it easier to use the dataset for supervised learning tasks.

Download Dataset

Applications:

This dataset is ideal for a variety of machine learning applications, including:

• Disease Detection: Training models to identify and classify various plant diseases, which can be pivotal for early detection and prevention.

• Image Classification: Developing and testing models to accurately classify images based on plant species and health status.

• Agricultural Research: Supporting research in precision agriculture by providing data that can lead to better understanding and management of plant health.

Dataset Structure:

• Organized Folders: The images are structured into folders corresponding to each plant species and disease type, facilitating easy access and manipulation of data.

• Healthy Samples Included: To ensure balanced datasets, healthy plant samples are included alongside diseased ones, enabling models to learn to differentiate between healthy and 
diseased plants.

• Versatile Use: The dataset's structure and labeling make it suitable for a wide range of research and commercial applications in agriculture and plant biology.

Conclusion:

The Plant Disease Dataset is a comprehensive and well-organized collection, ideal for anyone working on machine learning models in the field of agriculture. Whether you're developing new algorithms for disease detection or enhancing existing models, this dataset provides the rich and diverse data necessary to achieve accurate and reliable results.

This dataset is sourced from Kaggle.