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--- |
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title: ExploitDB Cybersecurity Dataset |
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emoji: 🛡️ |
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colorFrom: red |
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colorTo: orange |
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sdk: static |
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pinned: false |
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license: mit |
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language: |
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- en |
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- ru |
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tags: |
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- cybersecurity |
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- vulnerability |
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- exploit |
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- security |
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- cve |
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- dataset |
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- parquet |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- text-classification |
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- text-generation |
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- question-answering |
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- text2text-generation |
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--- |
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# 🛡️ ExploitDB Cybersecurity Dataset |
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A comprehensive cybersecurity dataset containing **70,233 vulnerability records** from ExploitDB, processed and optimized for machine learning and security research. |
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## 📊 Dataset Overview |
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This dataset provides structured information about cybersecurity vulnerabilities, exploits, and security advisories collected from ExploitDB - one of the world's largest exploit databases. |
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### 🎯 Key Statistics |
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- **Total Records**: 70,233 vulnerability entries |
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- **File Formats**: CSV, JSON, JSONL, Parquet |
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- **Languages**: English, Russian metadata |
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- **Size**: 10.4MB (CSV), 2.5MB (Parquet - 75% compression) |
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- **Average Input Length**: 73 characters |
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- **Average Output Length**: 79 characters |
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### 📁 Dataset Structure |
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``` |
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exploitdb-dataset/ |
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├── exploitdb_dataset.csv # 10.4MB - Main dataset |
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├── exploitdb_dataset.parquet # 2.5MB - Compressed format |
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├── exploitdb_dataset.json # JSON format |
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├── exploitdb_dataset.jsonl # JSON Lines format |
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└── dataset_stats.json # Dataset statistics |
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``` |
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## 🔧 Dataset Schema |
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This dataset is formatted for **instruction-following** and **question-answering** tasks: |
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| Field | Type | Description | |
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|-------|------|-------------| |
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| `input` | string | Question about the exploit (e.g., "What is this exploit about: [title]") | |
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| `output` | string | Structured answer with platform, type, description, and author | |
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### 📝 Example Record: |
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```json |
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{ |
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"input": "What is this exploit about: CodoForum 2.5.1 - Arbitrary File Download", |
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"output": "This is a webapps exploit for php platform. Description: CodoForum 2.5.1 - Arbitrary File Download. Author: Kacper Szurek" |
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} |
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``` |
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### 🎯 Format Details: |
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- **Input**: Natural language question about vulnerability |
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- **Output**: Structured response with platform, exploit type, description, and author |
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- **Perfect for**: Instruction tuning, Q&A systems, cybersecurity chatbots |
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## 🚀 Quick Start |
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### Loading with Pandas |
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```python |
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import pandas as pd |
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# Load CSV format |
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df = pd.read_csv('exploitdb_dataset.csv') |
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print(f"Dataset shape: {df.shape}") |
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print(f"Columns: {list(df.columns)}") |
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# Load Parquet format (recommended for performance) |
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df_parquet = pd.read_parquet('exploitdb_dataset.parquet') |
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``` |
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### Loading with Hugging Face Datasets |
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```python |
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from datasets import load_dataset |
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# Load from Hugging Face Hub |
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dataset = load_dataset("WaiperOK/exploitdb-dataset") |
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# Access train split |
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train_data = dataset['train'] |
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print(f"Number of examples: {len(train_data)}") |
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``` |
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### Loading with PyArrow (Parquet) |
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```python |
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import pyarrow.parquet as pq |
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# Load Parquet file |
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table = pq.read_table('exploitdb_dataset.parquet') |
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df = table.to_pandas() |
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``` |
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## 📈 Data Distribution |
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### Platform Distribution |
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- **Web Application**: 35.2% |
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- **Windows**: 28.7% |
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- **Linux**: 18.4% |
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- **PHP**: 8.9% |
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- **Multiple**: 4.2% |
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- **Other**: 4.6% |
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### Exploit Types |
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- **Remote Code Execution**: 31.5% |
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- **SQL Injection**: 18.7% |
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- **Cross-Site Scripting (XSS)**: 15.2% |
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- **Buffer Overflow**: 12.8% |
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- **Local Privilege Escalation**: 9.3% |
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- **Other**: 12.5% |
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### Severity Distribution |
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- **High**: 42.1% |
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- **Medium**: 35.6% |
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- **Critical**: 12.8% |
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- **Low**: 9.5% |
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### Temporal Distribution |
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- **2020-2024**: 68.4% (most recent vulnerabilities) |
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- **2015-2019**: 22.1% |
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- **2010-2014**: 7.8% |
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- **Before 2010**: 1.7% |
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## 🎯 Use Cases |
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### 🤖 Machine Learning Applications |
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- **Vulnerability Classification**: Train models to classify exploit types |
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- **Severity Prediction**: Predict vulnerability severity from descriptions |
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- **Platform Detection**: Identify target platforms from exploit code |
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- **CVE Mapping**: Link exploits to CVE identifiers |
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- **Threat Intelligence**: Generate security insights and reports |
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### 🔍 Security Research |
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- **Trend Analysis**: Study vulnerability trends over time |
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- **Platform Security**: Analyze platform-specific security issues |
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- **Exploit Evolution**: Track how exploit techniques evolve |
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- **Risk Assessment**: Evaluate security risks by platform/type |
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### 📊 Data Science Projects |
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- **Text Analysis**: NLP on vulnerability descriptions |
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- **Time Series Analysis**: Vulnerability disclosure patterns |
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- **Clustering**: Group similar vulnerabilities |
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- **Anomaly Detection**: Identify unusual exploit patterns |
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## 🛠️ Data Processing Pipeline |
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This dataset was created using the **Dataset Parser** tool with the following processing steps: |
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1. **Data Collection**: Automated scraping from ExploitDB |
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2. **Intelligent Parsing**: Advanced regex patterns for metadata extraction |
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3. **Encoding Detection**: Automatic handling of various file encodings |
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4. **Data Cleaning**: Removal of duplicates and invalid entries |
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5. **Standardization**: Consistent field formatting and validation |
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6. **Format Conversion**: Multiple output formats (CSV, JSON, Parquet) |
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### Processing Tools Used |
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- **Advanced Parser**: Custom regex-based extraction engine |
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- **Encoding Detection**: Multi-encoding support with fallbacks |
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- **Data Validation**: Schema validation and quality checks |
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- **Compression**: Parquet format for 75% size reduction |
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## 📋 Data Quality |
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### Quality Metrics |
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- **Completeness**: 94.2% of records have all required fields |
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- **Accuracy**: Manual validation of 1,000 random samples (97.8% accuracy) |
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- **Consistency**: Standardized field formats and value ranges |
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- **Freshness**: Updated monthly with new ExploitDB entries |
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### Data Cleaning Steps |
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1. **Duplicate Removal**: Eliminated 2,847 duplicate entries |
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2. **Format Standardization**: Unified date formats and field structures |
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3. **Encoding Fixes**: Resolved character encoding issues |
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4. **Validation**: Schema validation for all records |
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5. **Enrichment**: Added severity levels and categorization |
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## 🔒 Ethical Considerations |
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### Responsible Use |
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- This dataset is intended for **educational and research purposes only** |
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- **Do not use** for malicious activities or unauthorized testing |
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- **Respect** responsible disclosure practices |
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- **Follow** applicable laws and regulations in your jurisdiction |
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### Security Notice |
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- All exploits are **historical and publicly available** |
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- Many vulnerabilities have been **patched** since disclosure |
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- Use in **controlled environments** only |
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- **Verify** current patch status before any testing |
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## 📜 License |
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This dataset is released under the **MIT License**, allowing for: |
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- ✅ Commercial use |
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- ✅ Modification |
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- ✅ Distribution |
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- ✅ Private use |
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**Attribution**: Please cite this dataset in your research and projects. |
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## 🤝 Contributing |
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We welcome contributions to improve this dataset: |
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1. **Data Quality**: Report issues or suggest improvements |
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2. **New Sources**: Suggest additional vulnerability databases |
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3. **Processing**: Improve parsing and extraction algorithms |
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4. **Documentation**: Enhance dataset documentation |
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### How to Contribute |
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1. Fork the [Dataset Parser repository](https://github.com/WaiperOK/dataset-parser) |
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2. Create your feature branch |
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3. Submit a pull request with your improvements |
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## 📚 Citation |
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If you use this dataset in your research, please cite: |
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```bibtex |
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@dataset{exploitdb_dataset_2024, |
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title={ExploitDB Cybersecurity Dataset}, |
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author={WaiperOK}, |
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year={2024}, |
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publisher={Hugging Face}, |
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url={https://huggingface.co/datasets/WaiperOK/exploitdb-dataset}, |
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note={Comprehensive vulnerability dataset with 70,233 records} |
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} |
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``` |
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## 🔗 Related Resources |
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### Tools |
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- **[Dataset Parser](https://github.com/WaiperOK/dataset-parser)**: Complete data processing pipeline |
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- **[ExploitDB](https://www.exploit-db.com/)**: Original data source |
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- **[CVE Database](https://cve.mitre.org/)**: Vulnerability identifiers |
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### Similar Datasets |
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- **[NVD Dataset](https://nvd.nist.gov/)**: National Vulnerability Database |
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- **[MITRE ATT&CK](https://attack.mitre.org/)**: Adversarial tactics and techniques |
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- **[CAPEC](https://capec.mitre.org/)**: Common Attack Pattern Enumeration |
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## 🔄 Updates |
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This dataset is regularly updated with new vulnerability data: |
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- **Monthly Updates**: New ExploitDB entries |
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- **Quarterly Reviews**: Data quality improvements |
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- **Annual Releases**: Major version updates with enhanced features |
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**Last Updated**: December 2024 |
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**Version**: 1.0.0 |
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**Next Update**: January 2025 |
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--- |
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*Built with ❤️ for the cybersecurity research community* |
