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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ title: ExploitDB Cybersecurity Dataset
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+ emoji: πŸ›‘οΈ
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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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+ - information-extraction
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+ ---
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+
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+ # πŸ›‘οΈ ExploitDB Cybersecurity Dataset
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+
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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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+
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+ ## πŸ“Š Dataset Overview
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+
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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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+
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+ ### 🎯 Key Statistics
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+
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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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+
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+ ### πŸ“ Dataset Structure
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+
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+ ```
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+ exploitdb-dataset/
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+ β”œβ”€β”€ exploitdb_dataset.csv # 10.4MB - Main dataset
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+ β”œβ”€β”€ exploitdb_dataset.parquet # 2.5MB - Compressed format
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+ β”œβ”€β”€ exploitdb_dataset.json # JSON format
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+ β”œβ”€β”€ exploitdb_dataset.jsonl # JSON Lines format
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+ └── dataset_stats.json # Dataset statistics
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+ ```
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+
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+ ## πŸ”§ Dataset Schema
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+
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+ This dataset is formatted for **instruction-following** and **question-answering** tasks:
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+
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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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+
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+ ### πŸ“ 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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+
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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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+
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+ ## πŸš€ Quick Start
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+
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+ ### Loading with Pandas
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+
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+ ```python
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+ import pandas as pd
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+
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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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+
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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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+
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+ ### Loading with Hugging Face Datasets
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+
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+ ```python
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+ from datasets import load_dataset
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+
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+ # Load from Hugging Face Hub
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+ dataset = load_dataset("WaiperOK/exploitdb-dataset")
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+
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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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+
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+ ### Loading with PyArrow (Parquet)
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+
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+ ```python
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+ import pyarrow.parquet as pq
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+
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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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+
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+ ## πŸ“ˆ Data Distribution
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+
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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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+
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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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+
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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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+
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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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+
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+ ## 🎯 Use Cases
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+
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+ ### πŸ€– 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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+
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+ ### πŸ” 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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+
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+ ### πŸ“Š 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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+
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+ ## πŸ› οΈ Data Processing Pipeline
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+
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+ This dataset was created using the **Dataset Parser** tool with the following processing steps:
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+
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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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+
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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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+
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+ ## πŸ“‹ Data Quality
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+
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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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+
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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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+
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+ ## πŸ”’ Ethical Considerations
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+
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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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+
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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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+
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+ ## πŸ“œ License
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+
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+ This dataset is released under the **MIT License**, allowing for:
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+ - βœ… Commercial use
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+ - βœ… Modification
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+ - βœ… Distribution
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+ - βœ… Private use
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+
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+ **Attribution**: Please cite this dataset in your research and projects.
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+
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+ ## 🀝 Contributing
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+
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+ We welcome contributions to improve this dataset:
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+
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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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+
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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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+
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+ ## πŸ“š Citation
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+
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+ If you use this dataset in your research, please cite:
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+
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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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+
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+ ## πŸ”— Related Resources
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+
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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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+
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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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+
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+
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+ ## πŸ”„ Updates
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+
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+ This dataset is regularly updated with new vulnerability data:
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+
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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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+
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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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+ ---
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+
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+ *Built with ❀️ for the cybersecurity research community*