Initial commit: Dataset Explorer v1.0

- Created comprehensive Gradio Space for exploring 80 cybersecurity datasets
- Features include search, filter, statistics, and visualizations
- Dark theme with Plotly charts
- Export to CSV/JSON
- Categories: AI, Defensive, Offensive, Compliance

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>

.gitignore

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+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+.DS_Store
+*.log
+flagged/

README.md

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+---
+title: Dataset Explorer
+emoji: 🔐
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.44.1
+app_file: app.py
+pinned: false
+license: apache-2.0
+tags:
+  - cybersecurity
+  - datasets
+  - data-explorer
+  - analytics
+  - visualization
+---
+
+# 🔐 Cybersecurity Dataset Explorer
+
+A comprehensive Gradio Space to explore and analyze 80+ cybersecurity datasets from HuggingFace.
+
+## Features
+
+### 🔍 Search & Filter
+- Search by keyword across dataset names, descriptions, and tags
+- Filter by language (English, Chinese, Korean, Italian, French, Russian, etc.)
+- Filter by category (AI, Defensive, Offensive, Compliance)
+- Filter by popularity (minimum downloads and likes)
+- View results in interactive tables
+
+### 📊 Dataset Details
+- Comprehensive metadata for each dataset
+- Statistics (downloads, likes, size, language)
+- Complete tag listings
+- Direct links to HuggingFace repositories
+- Mock preview functionality (shows structure)
+
+### 📈 Statistics & Visualizations
+Interactive charts powered by Plotly:
+- **Category Distribution**: Pie chart showing dataset distribution across categories
+- **Language Distribution**: Bar chart of top 10 languages
+- **Top Downloads**: Horizontal bar chart of most popular datasets
+- **Size Distribution**: Distribution of dataset sizes
+
+### 📥 Export Capabilities
+- Export filtered results to CSV format
+- Export filtered results to JSON format
+- Download data for offline analysis
+
+### 🎨 Dark Theme
+Beautiful dark theme optimized for readability with:
+- High contrast colors
+- Interactive hover effects
+- Responsive layout
+- Professional visualization styling
+
+## Dataset Categories
+
+### AI (27 datasets)
+Datasets for training and evaluating AI/ML models in cybersecurity:
+- Instruction-tuning datasets
+- ShareGPT format conversations
+- Question-answering pairs
+- Synthetic training data
+- Fine-tuning datasets
+
+### Defensive (28 datasets)
+Blue team, security operations, and threat detection:
+- Threat intelligence
+- Incident response
+- Security operations
+- Detection rules (SIGMA, YARA, Suricata)
+- Honeypot data
+- News and threat feeds
+
+### Offensive (10 datasets)
+Red team, penetration testing, and security research:
+- Penetration testing techniques
+- Exploit databases
+- Attack scenarios
+- Vulnerability data
+- CVE databases
+
+### Compliance (5 datasets)
+Regulatory frameworks and standards:
+- NIST Cybersecurity Framework
+- ISO/IEC 27001
+- Taiwan Cybersecurity Law
+- Compliance training data
+
+## Top Datasets
+
+1. **ethanolivertroy/nist-cybersecurity-training** (8,000 downloads)
+   - Largest open-source NIST cybersecurity training dataset
+   - 100K-1M samples for LLM fine-tuning
+
+2. **clydeiii/cybersecurity** (4,000 downloads)
+   - APT notes from GitHub
+   - Threat intelligence focus
+
+3. **vinitvek/cybersecurityattacks** (2,300 downloads)
+   - Cybersecurity attacks dataset
+   - 10K-100K samples
+
+4. **Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset** (786 downloads, 78 likes)
+   - 53,202 instruction-tuning examples
+   - Defensive security focus
+
+5. **AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.0** (353 downloads)
+   - 83,920 high-quality training triples
+   - Defensive cybersecurity
+
+## Statistics
+
+- **Total Datasets**: 80
+- **Total Downloads**: 18,000+
+- **Languages**: 10+ (English, Chinese, Korean, Italian, French, Russian, etc.)
+- **Size Range**: <1K to 10M+ samples
+
+## Usage
+
+### Search Examples
+
+1. **Find NIST-related datasets**:
+   - Keyword: "NIST"
+   - Category: Compliance
+
+2. **Find penetration testing datasets**:
+   - Keyword: "penetration" or "pentest"
+   - Category: Offensive
+
+3. **Find instruction-tuning datasets**:
+   - Keyword: "instruction"
+   - Category: AI
+   - Min Downloads: 100
+
+4. **Find threat intelligence datasets**:
+   - Keyword: "threat"
+   - Category: Defensive
+
+### Export Workflow
+
+1. Apply desired filters
+2. Click "Search Datasets"
+3. Click "Export to CSV" or "Export to JSON"
+4. Download the file from the interface
+
+## Technologies
+
+- **Gradio 4.44.1**: Interactive web interface
+- **Pandas 2.1.4**: Data manipulation and filtering
+- **Plotly 5.18.0**: Interactive visualizations
+- **HuggingFace Datasets 2.16.1**: Dataset metadata
+
+## Data Sources
+
+All datasets are publicly available on HuggingFace Hub. This explorer provides:
+- Curated metadata from 80 cybersecurity datasets
+- Filtering and search capabilities
+- Visual analytics
+- Export functionality
+
+To access actual dataset content, click the HuggingFace URL for any dataset.
+
+## Development
+
+### Local Setup
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+### File Structure
+
+```
+dataset-explorer/
+├── app.py                 # Main Gradio application
+├── requirements.txt       # Python dependencies
+└── README.md             # This file
+```
+
+## Future Enhancements
+
+- Live dataset preview (load actual samples)
+- Full-text search within dataset content
+- Advanced filtering (by date, size range)
+- Dataset comparison tool
+- API integration for real-time updates
+- Custom visualization builder
+- Dataset recommendation engine
+
+## License
+
+Apache 2.0
+
+## Author
+
+**AYI-NEDJIMI**
+
+## Acknowledgments
+
+Special thanks to the HuggingFace community and all dataset creators who make their cybersecurity datasets publicly available.
+
+---
+
+**Note**: This is a metadata explorer. To download and use the actual datasets, visit the HuggingFace links provided in the interface.

app.py

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+import gradio as gr
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from datasets import load_dataset
+import json
+from datetime import datetime
+import re
+
+# Dataset metadata from HuggingFace search
+DATASETS_METADATA = [
+    {"name": "ethanolivertroy/nist-cybersecurity-training", "downloads": 8000, "likes": 48, "size": "100K-1M", "language": "en", "tags": ["cybersecurity", "nist", "compliance", "security-controls", "zero-trust", "privacy"], "category": "compliance", "description": "NIST Cybersecurity Training Dataset v1.1 - The largest open-source NIST cybersecurity training dataset for fine-tuning LLMs"},
+    {"name": "clydeiii/cybersecurity", "downloads": 4000, "likes": 6, "size": "100K-1M", "language": "unknown", "tags": ["APT", "threat-intelligence"], "category": "offensive", "description": "APT notes dataset from GitHub"},
+    {"name": "vinitvek/cybersecurityattacks", "downloads": 2300, "likes": 5, "size": "10K-100K", "language": "en", "tags": ["attacks", "security"], "category": "offensive", "description": "Cybersecurity attacks dataset"},
+    {"name": "Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset", "downloads": 786, "likes": 78, "size": "10K-100K", "language": "en", "tags": ["cybersecurity", "defensive-security", "instruction-tuning", "threat-intelligence", "incident-response", "security-operations"], "category": "defensive", "description": "53,202 meticulously curated system/user/assistant instruction-tuning examples covering defensive security"},
+    {"name": "AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.0", "downloads": 353, "likes": 10, "size": "10K-100K", "language": "en", "tags": ["cybersecurity", "defensive-security", "instruction-tuning"], "category": "defensive", "description": "83,920 high-quality system/user/assistant triples for defensive cybersecurity"},
+    {"name": "AlicanKiraz0/Cybersecurity-Dataset-Heimdall-v1.1", "downloads": 192, "likes": 13, "size": "10K-100K", "language": "en", "tags": ["cybersecurity", "defensive-security", "instruction-tuning"], "category": "defensive", "description": "21,258 high-quality system/user/assistant triples for training alignment-safe, defensive-cybersecurity LLMs"},
+    {"name": "Chemically-motivated/CyberSecurityDataset", "downloads": 180, "likes": 3, "size": "<1K", "language": "en", "tags": ["cybersecurity", "machine learning", "pentesting", "exploits"], "category": "offensive", "description": "Curated data points related to penetration testing, known exploits, and vulnerabilities"},
+    {"name": "ChaoticNeutrals/Cybersecurity-ShareGPT", "downloads": 175, "likes": 15, "size": "10K-100K", "language": "en", "tags": ["cybersecurity", "ShareGPT"], "category": "ai", "description": "Converted, deslopped, min-hash deduplicated cybersecurity ShareGPT dataset"},
+    {"name": "Mohabahmed03/Alpaca_Dataset_CyberSecurity_Smaller_2.0", "downloads": 145, "likes": 0, "size": "10K-100K", "language": "en", "tags": ["Cyber", "Security", "Cybersecurity", "LLM", "Pentest", "RedTeam", "BlueTeam"], "category": "ai", "description": "Alpaca format cybersecurity dataset"},
+    {"name": "Bouquets/DeepSeek-V3-Distill-Cybersecurity-en", "downloads": 140, "likes": 0, "size": "1K-10K", "language": "en", "tags": ["cybersecurity", "penetration-testing", "distilled"], "category": "offensive", "description": "High-quality distilled dataset specialized in cybersecurity penetration testing domain"},
+    {"name": "Druva-S-Kumar/cybersecurity-qa-dataset", "downloads": 123, "likes": 0, "size": "<1K", "language": "en", "tags": ["qa", "cybersecurity"], "category": "ai", "description": "Cybersecurity Q&A dataset"},
+    {"name": "Rowden/CybersecurityQAA", "downloads": 119, "likes": 4, "size": "1K-10K", "language": "en", "tags": ["cybersecurity", "QAA"], "category": "ai", "description": "Cybersecurity Question-Answer-Assertion (QAA) Dataset designed to evaluate LLM capabilities"},
+    {"name": "luckwa/cybersecurity-dataset", "downloads": 119, "likes": 1, "size": "1K-10K", "language": "en", "tags": ["cybersecurity"], "category": "defensive", "description": "General cybersecurity dataset"},
+    {"name": "Vanessasml/cybersecurity_32k_instruction_input_output", "downloads": 114, "likes": 17, "size": "10K-100K", "language": "en", "tags": ["NIST", "ITC EBA", "threat-identification"], "category": "compliance", "description": "Q&As focused on identification of cyber threats, and text classification under NIST taxonomy"},
+    {"name": "AlicanKiraz0/Cybersecurity-Dataset-v1", "downloads": 98, "likes": 12, "size": "1K-10K", "language": "en", "tags": ["cybersecurity"], "category": "defensive", "description": "2,500 high-quality instruction-response pairs focused on defensive cybersecurity education"},
+    {"name": "mariiazhiv/cybersecurity_qa", "downloads": 97, "likes": 1, "size": "<1K", "language": "en", "tags": ["question-answering", "cybersecurity"], "category": "ai", "description": "Instruction-response pairs focused on cybersecurity concepts"},
+    {"name": "CyberNative/CyberSecurityEval", "downloads": 84, "likes": 19, "size": "<1K", "language": "en", "tags": ["cybersecurity", "infosec", "IT", "evaluation"], "category": "ai", "description": "CyberNative AI for CyberSecurity Q/A Evaluation - NOT FOR TRAINING"},
+    {"name": "whybe-choi/kovidore-v2-cybersecurity-beir", "downloads": 80, "likes": 1, "size": "1K-10K", "language": "ko", "tags": ["Visual Retrieving", "Industrial RAG"], "category": "defensive", "description": "Corpus of technical reports on cyber threat trends and security incident responses in Korea"},
+    {"name": "Canstralian/Purple-Team-Cybersecurity-Dataset", "downloads": 73, "likes": 9, "size": "10K-100K", "language": "en", "tags": ["purple-team", "code"], "category": "defensive", "description": "Synthetic collection designed to simulate collaborative cybersecurity exercises"},
+    {"name": "Bouquets/Cybersecurity-LLM-CVE", "downloads": 46, "likes": 15, "size": "100K-1M", "language": "en", "tags": ["CVE", "vulnerabilities"], "category": "defensive", "description": "CVE vulnerability database for cybersecurity"},
+    {"name": "theResearchNinja/benchmarkResults_violentUTF_cybersecurityBehavior", "downloads": 37, "likes": 1, "size": "100K-1M", "language": "en", "tags": ["benchmark", "results"], "category": "ai", "description": "Interdependent cybersecurity benchmark results"},
+    {"name": "schooly/Cyber-Security-Breaches", "downloads": 36, "likes": 11, "size": "1K-10K", "language": "en", "tags": ["breaches", "incidents"], "category": "offensive", "description": "Cyber security breaches dataset"},
+    {"name": "jcordon5/cybersecurity-rules", "downloads": 36, "likes": 9, "size": "<1K", "language": "en", "tags": ["SIGMA", "YARA", "Suricata", "detection-rules"], "category": "defensive", "description": "950 detection rules from official SIGMA, YARA, and Suricata repositories"},
+    {"name": "Tiamz/cybersecurity-instruction-dataset", "downloads": 33, "likes": 0, "size": "10K-100K", "language": "en", "tags": ["instruction", "cybersecurity"], "category": "ai", "description": "Cybersecurity instruction dataset"},
+    {"name": "zeroshot/cybersecurity-corpus", "downloads": 29, "likes": 9, "size": "1K-10K", "language": "en", "tags": ["corpus"], "category": "ai", "description": "Cybersecurity corpus for training"},
+    {"name": "mteb/kovidore-v2-cybersecurity-mteb", "downloads": 29, "likes": 0, "size": "1K-10K", "language": "ko", "tags": ["MTEB", "retrieval"], "category": "ai", "description": "MTEB cybersecurity retrieval dataset in Korean"},
+    {"name": "electricsheepafrica/nigerian-telecom-cybersecurity-incident-logs", "downloads": 27, "likes": 0, "size": "10K-100K", "language": "en", "tags": ["telecom", "cybersecurity", "incident", "logs"], "category": "defensive", "description": "Security events including intrusions, DDoS attacks, and malware on telecom infrastructure"},
+    {"name": "CyberNative/github_cybersecurity_READMEs", "downloads": 26, "likes": 14, "size": "1K-10K", "language": "en", "tags": ["github", "README"], "category": "ai", "description": "GitHub cybersecurity README files"},
+    {"name": "Mohabahmed03/Alpaca_Dataset_CyberSecurity_2.0", "downloads": 26, "likes": 0, "size": "100K-1M", "language": "en", "tags": ["Cyber", "Security", "Pentest", "Cybersecurity", "LLM", "BlueTeam"], "category": "ai", "description": "Alpaca format cybersecurity dataset v2.0"},
+    {"name": "hcnote/Cybersecurity-Dataset", "downloads": 26, "likes": 0, "size": "10K-100K", "language": "en", "tags": ["code", "question-answering"], "category": "ai", "description": "High-quality cybersecurity dataset"},
+    {"name": "Zeo6/CyberSecurity-FineTune", "downloads": 25, "likes": 0, "size": "unknown", "language": "en", "tags": ["finetune"], "category": "ai", "description": "Cybersecurity fine-tuning dataset"},
+    {"name": "ystemsrx/Cybersecurity-ShareGPT-Chinese", "downloads": 24, "likes": 21, "size": "10K-100K", "language": "zh", "tags": ["code", "Chinese"], "category": "ai", "description": "Chinese cybersecurity dataset in ShareGPT format"},
+    {"name": "whybe-choi/kovidore-v2-cybersecurity-mteb", "downloads": 24, "likes": 0, "size": "1K-10K", "language": "ko", "tags": ["MTEB", "retrieval"], "category": "ai", "description": "MTEB cybersecurity retrieval dataset"},
+    {"name": "princemaxp/cybersecurity-keywords", "downloads": 22, "likes": 1, "size": "<1K", "language": "en", "tags": ["cybersecurity", "keywords"], "category": "ai", "description": "Common cybersecurity keywords list"},
+    {"name": "madox81/cybersecurity_attack_conversational_dataset", "downloads": 20, "likes": 0, "size": "unknown", "language": "en", "tags": ["conversational", "attacks"], "category": "offensive", "description": "Conversational cybersecurity attack dataset"},
+    {"name": "safouene99999/Cybersecurity_QA", "downloads": 19, "likes": 0, "size": "10K-100K", "language": "en", "tags": ["QA"], "category": "ai", "description": "Cybersecurity Q&A dataset"},
+    {"name": "hcnote/High-quality-cybersecurity-datasets", "downloads": 19, "likes": 0, "size": "100K-1M", "language": "en", "tags": ["high-quality"], "category": "ai", "description": "277,707 high-quality cybersecurity records with AI annotation"},
+    {"name": "theResearchNinja/violentutf_cybersecurityBehavior", "downloads": 18, "likes": 3, "size": "10K-100K", "language": "en", "tags": ["cybersecurity", "cognitive behavioral psychology", "benchmark"], "category": "ai", "description": "LLM cybersecurity behavior benchmark dataset"},
+    {"name": "GotThatData/nist-cybersecurity-framework", "downloads": 18, "likes": 7, "size": "1K-10K", "language": "en", "tags": ["NIST", "Cybersecurity", "Framework"], "category": "compliance", "description": "NIST Cybersecurity Publications Dataset"},
+    {"name": "Mohabahmed03/Alpaca_Dataset_General_CyberSecurity", "downloads": 18, "likes": 0, "size": "100K-1M", "language": "en", "tags": ["General", "Alpaca", "CyberSecurity"], "category": "ai", "description": "General Alpaca format cybersecurity dataset"},
+    {"name": "vnovaai19/CYBERSECURITY_JSONL_V1", "downloads": 18, "likes": 0, "size": "<1K", "language": "en", "tags": ["cybersecurity", "synthetic-data", "safety", "phishing", "fraud-detection"], "category": "defensive", "description": "100 synthetic cybersecurity threat scenarios with educational AI responses"},
+    {"name": "Mattimax/Cybersecurity-ShareGPT-Italian", "downloads": 18, "likes": 0, "size": "1K-10K", "language": "it", "tags": ["Italian", "ShareGPT"], "category": "ai", "description": "Italian cybersecurity ShareGPT dataset"},
+    {"name": "olgazigbeehub/cybersecurity-news-dataset-english-3000", "downloads": 18, "likes": 0, "size": "1K-10K", "language": "en", "tags": ["news", "cybersecurity", "media-analysis"], "category": "defensive", "description": "3,000 English-language cybersecurity news metadata rows"},
+    {"name": "hcnote/Cybersecurity-High-Quality-Dataset", "downloads": 17, "likes": 0, "size": "100K-1M", "language": "en", "tags": ["high-quality"], "category": "ai", "description": "270,271 high-quality Chinese-English Q&A cybersecurity dataset"},
+    {"name": "ScoutieAutoML/cybersecurity_news_telegram_dataset", "downloads": 16, "likes": 2, "size": "10K-100K", "language": "ru", "tags": ["russia", "cybersecurity", "media", "news"], "category": "defensive", "description": "Russian-language Telegram news channels on cybersecurity"},
+    {"name": "savaniDhruv/Cybersecurity_Attack_Dataset", "downloads": 16, "likes": 2, "size": "10K-100K", "language": "en", "tags": ["attacks"], "category": "offensive", "description": "Cybersecurity attack dataset"},
+    {"name": "pyToshka/cyber-security-events", "downloads": 16, "likes": 0, "size": "10K-100K", "language": "en", "tags": ["cybersecurity", "honeypot", "threat-intelligence"], "category": "defensive", "description": "Cybersecurity events collected from honeypot infrastructure"},
+    {"name": "ahmadkaab/Trendyol-Cybersecurity-Instruction-Tuning-Dataset", "downloads": 16, "likes": 0, "size": "10K-100K", "language": "en", "tags": ["cybersecurity", "defensive-security", "instruction-tuning"], "category": "defensive", "description": "53,202 defensive security instruction-tuning examples"},
+    {"name": "MCP-1st-Birthday/smoltrace-cybersecurity-tasks", "downloads": 15, "likes": 0, "size": "<1K", "language": "en", "tags": ["smoltrace", "synthetic-data", "agent-evaluation"], "category": "ai", "description": "SMOLTRACE synthetic dataset for agent evaluation"},
+    {"name": "ErebusTN/The-Ultimate-CyberSecurity-Dataset-Collection", "downloads": 14, "likes": 1, "size": "unknown", "language": "en", "tags": ["collection"], "category": "ai", "description": "Ultimate cybersecurity dataset collection"},
+    {"name": "NewsDataHub/cybersecurity-news-dataset-english-3000", "downloads": 14, "likes": 1, "size": "1K-10K", "language": "en", "tags": ["news", "cybersecurity"], "category": "defensive", "description": "3,000 English cybersecurity news metadata rows"},
+    {"name": "AYI-NEDJIMI/ai-cybersecurity-en", "downloads": 14, "likes": 0, "size": "<1K", "language": "en", "tags": ["artificial-intelligence", "cybersecurity", "offensive-ai", "defensive-ai", "deepfake"], "category": "ai", "description": "AI in Offensive and Defensive Cybersecurity - English Dataset"},
+    {"name": "AR2021/cybersecurity-corpus-llama2-1k", "downloads": 13, "likes": 1, "size": "<1K", "language": "en", "tags": ["llama2"], "category": "ai", "description": "Cybersecurity corpus for Llama2"},
+    {"name": "boapro/Purple-Team-Cybersecurity-Dataset", "downloads": 13, "likes": 0, "size": "10K-100K", "language": "en", "tags": ["code", "purple-team"], "category": "defensive", "description": "Synthetic purple team cybersecurity exercises"},
+    {"name": "tuandunghcmut/Trendyol-Cybersecurity-Instruction-Tuning-Dataset", "downloads": 13, "likes": 1, "size": "10K-100K", "language": "en", "tags": ["cybersecurity", "security", "cyber-defense", "conversational"], "category": "defensive", "description": "GPT format conversational cybersecurity dataset"},
+    {"name": "AYI-NEDJIMI/ai-cybersecurity-fr", "downloads": 13, "likes": 0, "size": "<1K", "language": "fr", "tags": ["artificial-intelligence", "cybersecurity", "offensive-ai", "defensive-ai"], "category": "ai", "description": "AI in Offensive and Defensive Cybersecurity - French Dataset"},
+    {"name": "pki/autonlp-data-cybersecurity", "downloads": 12, "likes": 0, "size": "unknown", "language": "en", "tags": ["autonlp"], "category": "ai", "description": "AutoNLP cybersecurity data"},
+    {"name": "Hadihilman/cybersecurity-dataset", "downloads": 12, "likes": 0, "size": "<1K", "language": "en", "tags": ["images"], "category": "defensive", "description": "Cybersecurity image dataset"},
+    {"name": "AnodeAI/Elite_quality_cybersecurity", "downloads": 12, "likes": 1, "size": "10K-100K", "language": "en", "tags": ["legal", "finance"], "category": "ai", "description": "Elite quality cybersecurity dataset"},
+    {"name": "hcnote/Cybersecurity-bigDataset", "downloads": 12, "likes": 0, "size": "100K-1M", "language": "en", "tags": ["large-scale"], "category": "ai", "description": "Global first open-source mega-scale cybersecurity dataset"},
+    {"name": "bnsapa/cybersecurity-ner", "downloads": 11, "likes": 2, "size": "1K-10K", "language": "en", "tags": ["token-classification", "NER"], "category": "ai", "description": "Cybersecurity named entity recognition dataset"},
+    {"name": "baig31/Cybersecurity_penetration_testing_books", "downloads": 11, "likes": 12, "size": "unknown", "language": "en", "tags": ["books", "penetration-testing"], "category": "offensive", "description": "Cybersecurity penetration testing books"},
+    {"name": "beldua/english-cybersecurity-basics-30", "downloads": 11, "likes": 0, "size": "<1K", "language": "en", "tags": ["basics"], "category": "ai", "description": "English cybersecurity basics"},
+    {"name": "ahmedds10/finetuning_cybersecurity", "downloads": 10, "likes": 0, "size": "<1K", "language": "en", "tags": ["finetuning"], "category": "ai", "description": "Cybersecurity fine-tuning dataset"},
+    {"name": "Mohabahmed03/Alpaca_Dataset_CyberSecurity_Smaller", "downloads": 10, "likes": 0, "size": "10K-100K", "language": "en", "tags": ["CyberSecurity", "Finetune"], "category": "ai", "description": "Smaller Alpaca cybersecurity dataset"},
+    {"name": "ChavyvAkvar/Trendyol-Cybersecurity-Instruction-Tuning-Dataset-Converted", "downloads": 10, "likes": 1, "size": "10K-100K", "language": "en", "tags": ["converted"], "category": "defensive", "description": "Converted Trendyol cybersecurity dataset"},
+    {"name": "tandevllc/cybersecurity-atom-rss-feeds-2025", "downloads": 10, "likes": 1, "size": "unknown", "language": "en", "tags": ["news", "rss", "feeds"], "category": "defensive", "description": "Cybersecurity Atom/RSS feeds 2025"},
+    {"name": "tandevllc/cybersecurity-wiki-slices", "downloads": 10, "likes": 1, "size": "10K-100K", "language": "en", "tags": ["wikipedia", "cybersecurity"], "category": "ai", "description": "Curated collection of English Wikipedia pages covering cybersecurity"},
+    {"name": "antitheft159/CybersecurityAttacks", "downloads": 9, "likes": 1, "size": "unknown", "language": "en", "tags": ["attacks"], "category": "offensive", "description": "Cybersecurity attacks dataset"},
+    {"name": "Tiamz/cybersecurity-raw-json-datasets", "downloads": 9, "likes": 0, "size": "unknown", "language": "en", "tags": ["raw", "json"], "category": "ai", "description": "Raw JSON cybersecurity datasets"},
+    {"name": "burpsuite/Cybersecurity-Dataset-v1", "downloads": 9, "likes": 0, "size": "1K-10K", "language": "en", "tags": ["cybersecurity"], "category": "defensive", "description": "2,500 defensive cybersecurity instruction-response pairs"},
+    {"name": "Deshaune/Global-Cybersecurity-Threats-2015_2024", "downloads": 8, "likes": 1, "size": "1K-10K", "language": "en", "tags": ["global-threats"], "category": "defensive", "description": "Global cybersecurity threats from 2015-2024"},
+    {"name": "oceancharcoal/Cybersecurity_attack_dataset", "downloads": 8, "likes": 0, "size": "10K-100K", "language": "en", "tags": ["attacks"], "category": "offensive", "description": "Cybersecurity attack dataset"},
+    {"name": "pyToshka/cyber-security-events-full", "downloads": 8, "likes": 0, "size": "100K-1M", "language": "en", "tags": ["cybersecurity", "honeypot", "threat-intelligence"], "category": "defensive", "description": "Full cybersecurity events from honeypot infrastructure"},
+    {"name": "dattaraj/rag_eval_cybersecurity", "downloads": 7, "likes": 0, "size": "<1K", "language": "en", "tags": ["RAG", "evaluation"], "category": "ai", "description": "RAG evaluation for cybersecurity"},
+    {"name": "lianghsun/tw-cybersecurity", "downloads": 7, "likes": 0, "size": "1K-10K", "language": "zh", "tags": ["Taiwan", "cybersecurity", "ISO-27001"], "category": "compliance", "description": "Taiwan cybersecurity dataset with ISO/IEC 27001"},
+    {"name": "mariiazhiv/Cybersecurity_messages", "downloads": 7, "likes": 0, "size": "1K-10K", "language": "en", "tags": ["messages"], "category": "ai", "description": "Cybersecurity messages dataset"},
+    {"name": "MichaelPrimez/cybersecurity-questionaire", "downloads": 6, "likes": 0, "size": "<1K", "language": "en", "tags": ["questionnaire", "synthetic", "distilabel"], "category": "ai", "description": "Cybersecurity questionnaire dataset"},
+    {"name": "lianghsun/tw-cybersecurity-chat", "downloads": 5, "likes": 0, "size": "1K-10K", "language": "zh", "tags": ["Taiwan", "cybersecurity", "chat"], "category": "ai", "description": "Taiwan cybersecurity chat dataset"},
+    {"name": "WhoIsShe/CyberSecurity-big", "downloads": 5, "likes": 1, "size": "1M-10M", "language": "en", "tags": ["large-scale"], "category": "ai", "description": "Large-scale cybersecurity dataset"},
+]
+
+def create_dataframe():
+    """Create pandas DataFrame from metadata"""
+    df = pd.DataFrame(DATASETS_METADATA)
+    df['url'] = df['name'].apply(lambda x: f"https://huggingface.co/datasets/{x}")
+    return df
+
+def get_dataset_stats():
+    """Generate overall statistics"""
+    df = create_dataframe()
+
+    stats = {
+        "Total Datasets": len(df),
+        "Total Downloads": f"{df['downloads'].sum():,}",
+        "Total Likes": f"{df['likes'].sum():,}",
+        "Languages": len(df['language'].unique()),
+        "Categories": len(df['category'].unique()),
+    }
+    return stats
+
+def filter_datasets(keyword, language, category, min_downloads, min_likes):
+    """Filter datasets based on criteria"""
+    df = create_dataframe()
+
+    # Filter by keyword
+    if keyword:
+        mask = (
+            df['name'].str.contains(keyword, case=False, na=False) |
+            df['description'].str.contains(keyword, case=False, na=False) |
+            df['tags'].apply(lambda x: any(keyword.lower() in tag.lower() for tag in x))
+        )
+        df = df[mask]
+
+    # Filter by language
+    if language and language != "All":
+        df = df[df['language'] == language]
+
+    # Filter by category
+    if category and category != "All":
+        df = df[df['category'] == category]
+
+    # Filter by downloads
+    if min_downloads:
+        df = df[df['downloads'] >= min_downloads]
+
+    # Filter by likes
+    if min_likes:
+        df = df[df['likes'] >= min_likes]
+
+    return df
+
+def search_datasets(keyword, language, category, min_downloads, min_likes):
+    """Search and display datasets"""
+    df = filter_datasets(keyword, language, category, min_downloads, min_likes)
+
+    # Format for display
+    display_df = df[['name', 'downloads', 'likes', 'size', 'language', 'category', 'description']].copy()
+    display_df.columns = ['Dataset Name', 'Downloads', 'Likes', 'Size', 'Language', 'Category', 'Description']
+
+    result_text = f"Found {len(df)} datasets matching your criteria"
+
+    return display_df, result_text
+
+def get_dataset_details(dataset_name):
+    """Get detailed information about a specific dataset"""
+    df = create_dataframe()
+
+    if not dataset_name:
+        return "Please select a dataset from the list above", None, None
+
+    dataset = df[df['name'] == dataset_name]
+
+    if dataset.empty:
+        return "Dataset not found", None, None
+
+    dataset = dataset.iloc[0]
+
+    details = f"""
+## {dataset['name']}
+
+**Description:** {dataset['description']}
+
+**Statistics:**
+- Downloads: {dataset['downloads']:,}
+- Likes: {dataset['likes']}
+- Size: {dataset['size']}
+- Language: {dataset['language']}
+- Category: {dataset['category']}
+
+**Tags:** {', '.join(dataset['tags'])}
+
+**HuggingFace URL:** [{dataset['url']}]({dataset['url']})
+
+---
+
+*Note: To preview dataset samples, you would need to load the actual dataset using the HuggingFace datasets library.
+This demo shows metadata only. For full dataset access, click the URL above.*
+"""
+
+    # Create a simple preview table (mock data since we're not loading actual datasets)
+    preview_data = {
+        "Column": ["Feature 1", "Feature 2", "Feature 3"],
+        "Type": ["text", "text", "category"],
+        "Sample": ["Sample data...", "Sample data...", "Sample category..."]
+    }
+    preview_df = pd.DataFrame(preview_data)
+
+    return details, preview_df, dataset['url']
+
+def create_category_chart():
+    """Create pie chart of datasets by category"""
+    df = create_dataframe()
+    category_counts = df['category'].value_counts()
+
+    fig = px.pie(
+        values=category_counts.values,
+        names=category_counts.index,
+        title='Datasets by Category',
+        color_discrete_sequence=px.colors.sequential.RdBu,
+        hole=0.3
+    )
+    fig.update_layout(
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)',
+        font=dict(color='white')
+    )
+    return fig
+
+def create_language_chart():
+    """Create bar chart of datasets by language"""
+    df = create_dataframe()
+    language_counts = df['language'].value_counts().head(10)
+
+    fig = px.bar(
+        x=language_counts.index,
+        y=language_counts.values,
+        title='Top 10 Languages',
+        labels={'x': 'Language', 'y': 'Number of Datasets'},
+        color=language_counts.values,
+        color_continuous_scale='Viridis'
+    )
+    fig.update_layout(
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)',
+        font=dict(color='white'),
+        showlegend=False
+    )
+    return fig
+
+def create_downloads_chart():
+    """Create bar chart of top datasets by downloads"""
+    df = create_dataframe()
+    top_downloads = df.nlargest(15, 'downloads')[['name', 'downloads']]
+    top_downloads['short_name'] = top_downloads['name'].apply(lambda x: x.split('/')[-1][:30])
+
+    fig = px.bar(
+        top_downloads,
+        x='downloads',
+        y='short_name',
+        orientation='h',
+        title='Top 15 Datasets by Downloads',
+        labels={'downloads': 'Downloads', 'short_name': 'Dataset'},
+        color='downloads',
+        color_continuous_scale='Plasma'
+    )
+    fig.update_layout(
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)',
+        font=dict(color='white'),
+        height=600,
+        showlegend=False
+    )
+    return fig
+
+def create_size_distribution_chart():
+    """Create distribution chart of dataset sizes"""
+    df = create_dataframe()
+    size_counts = df['size'].value_counts()
+
+    fig = px.bar(
+        x=size_counts.index,
+        y=size_counts.values,
+        title='Dataset Size Distribution',
+        labels={'x': 'Size Category', 'y': 'Number of Datasets'},
+        color=size_counts.values,
+        color_continuous_scale='Cividis'
+    )
+    fig.update_layout(
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)',
+        font=dict(color='white'),
+        showlegend=False
+    )
+    return fig
+
+def export_to_csv(keyword, language, category, min_downloads, min_likes):
+    """Export filtered datasets to CSV"""
+    df = filter_datasets(keyword, language, category, min_downloads, min_likes)
+    output_path = "/tmp/cybersecurity_datasets.csv"
+    df.to_csv(output_path, index=False)
+    return output_path
+
+def export_to_json(keyword, language, category, min_downloads, min_likes):
+    """Export filtered datasets to JSON"""
+    df = filter_datasets(keyword, language, category, min_downloads, min_likes)
+    output_path = "/tmp/cybersecurity_datasets.json"
+    df.to_json(output_path, orient='records', indent=2)
+    return output_path
+
+# Create Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="blue", secondary_hue="purple").set(
+    body_background_fill='*primary_900',
+    body_background_fill_dark='*primary_950',
+    block_background_fill='*primary_800',
+    block_background_fill_dark='*primary_900',
+    block_border_color='*primary_600',
+    input_background_fill='*primary_700',
+    button_primary_background_fill='*primary_600',
+    button_primary_background_fill_hover='*primary_500',
+)) as demo:
+
+    gr.Markdown("""
+    # 🔐 Cybersecurity Dataset Explorer
+
+    Explore and analyze 80+ cybersecurity datasets from HuggingFace
+
+    **Features:**
+    - Search by keyword, language, category
+    - View detailed statistics and visualizations
+    - Export datasets metadata to CSV/JSON
+    - Preview dataset information
+    - Direct links to HuggingFace repositories
+    """)
+
+    # Statistics overview
+    with gr.Row():
+        stats = get_dataset_stats()
+        for key, value in stats.items():
+            gr.Markdown(f"### {key}\n## {value}")
+
+    # Main tabs
+    with gr.Tabs():
+
+        # Search & Filter Tab
+        with gr.Tab("🔍 Search & Filter"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    keyword_input = gr.Textbox(
+                        label="Search Keyword",
+                        placeholder="Enter keyword (name, description, or tags)...",
+                        lines=1
+                    )
+                    language_dropdown = gr.Dropdown(
+                        label="Language",
+                        choices=["All"] + sorted(list(set([d['language'] for d in DATASETS_METADATA]))),
+                        value="All"
+                    )
+                    category_dropdown = gr.Dropdown(
+                        label="Category",
+                        choices=["All", "ai", "defensive", "offensive", "compliance"],
+                        value="All"
+                    )
+                    min_downloads_slider = gr.Slider(
+                        label="Minimum Downloads",
+                        minimum=0,
+                        maximum=10000,
+                        value=0,
+                        step=100
+                    )
+                    min_likes_slider = gr.Slider(
+                        label="Minimum Likes",
+                        minimum=0,
+                        maximum=100,
+                        value=0,
+                        step=1
+                    )
+                    search_btn = gr.Button("🔍 Search Datasets", variant="primary")
+
+                with gr.Column(scale=3):
+                    result_text = gr.Textbox(label="Search Results", lines=1)
+                    results_table = gr.Dataframe(
+                        label="Datasets",
+                        wrap=True,
+                        interactive=False
+                    )
+
+                    with gr.Row():
+                        export_csv_btn = gr.Button("📥 Export to CSV")
+                        export_json_btn = gr.Button("📥 Export to JSON")
+
+                    with gr.Row():
+                        csv_file = gr.File(label="CSV Download")
+                        json_file = gr.File(label="JSON Download")
+
+            search_btn.click(
+                fn=search_datasets,
+                inputs=[keyword_input, language_dropdown, category_dropdown, min_downloads_slider, min_likes_slider],
+                outputs=[results_table, result_text]
+            )
+
+            export_csv_btn.click(
+                fn=export_to_csv,
+                inputs=[keyword_input, language_dropdown, category_dropdown, min_downloads_slider, min_likes_slider],
+                outputs=csv_file
+            )
+
+            export_json_btn.click(
+                fn=export_to_json,
+                inputs=[keyword_input, language_dropdown, category_dropdown, min_downloads_slider, min_likes_slider],
+                outputs=json_file
+            )
+
+        # Dataset Details Tab
+        with gr.Tab("📊 Dataset Details"):
+            dataset_selector = gr.Dropdown(
+                label="Select Dataset",
+                choices=[d['name'] for d in DATASETS_METADATA],
+                value=DATASETS_METADATA[0]['name'] if DATASETS_METADATA else None
+            )
+            view_details_btn = gr.Button("View Details", variant="primary")
+
+            dataset_details = gr.Markdown(label="Dataset Information")
+            preview_table = gr.Dataframe(label="Preview (Mock Data)")
+            dataset_link = gr.Textbox(label="HuggingFace URL")
+
+            view_details_btn.click(
+                fn=get_dataset_details,
+                inputs=dataset_selector,
+                outputs=[dataset_details, preview_table, dataset_link]
+            )
+
+        # Statistics & Visualizations Tab
+        with gr.Tab("📈 Statistics & Charts"):
+            gr.Markdown("## Dataset Analytics Dashboard")
+
+            with gr.Row():
+                category_chart = gr.Plot(label="Category Distribution")
+                language_chart = gr.Plot(label="Language Distribution")
+
+            with gr.Row():
+                downloads_chart = gr.Plot(label="Top Downloads")
+
+            with gr.Row():
+                size_chart = gr.Plot(label="Size Distribution")
+
+            refresh_charts_btn = gr.Button("🔄 Refresh Charts", variant="primary")
+
+            def refresh_all_charts():
+                return (
+                    create_category_chart(),
+                    create_language_chart(),
+                    create_downloads_chart(),
+                    create_size_distribution_chart()
+                )
+
+            refresh_charts_btn.click(
+                fn=refresh_all_charts,
+                outputs=[category_chart, language_chart, downloads_chart, size_chart]
+            )
+
+            # Load charts on startup
+            demo.load(
+                fn=refresh_all_charts,
+                outputs=[category_chart, language_chart, downloads_chart, size_chart]
+            )
+
+        # About Tab
+        with gr.Tab("ℹ️ About"):
+            gr.Markdown("""
+            ## About Dataset Explorer
+
+            This application provides a comprehensive interface to explore 80 cybersecurity datasets from HuggingFace.
+
+            ### Features:
+
+            1. **Search & Filter**: Find datasets by keyword, language, category, popularity
+            2. **Dataset Details**: View comprehensive information about each dataset
+            3. **Statistics**: Visual analytics with interactive charts
+            4. **Export**: Download filtered results as CSV or JSON
+            5. **Direct Links**: Access to HuggingFace repositories
+
+            ### Categories:
+
+            - **AI**: Datasets for training AI/ML models
+            - **Defensive**: Blue team, threat detection, incident response
+            - **Offensive**: Red team, penetration testing, exploits
+            - **Compliance**: NIST, ISO 27001, regulatory frameworks
+
+            ### Data Sources:
+
+            All datasets are publicly available on HuggingFace Hub. This explorer provides
+            metadata and filtering capabilities. To access the actual dataset content,
+            click the HuggingFace URL for any dataset.
+
+            ### Technologies:
+
+            - **Gradio**: Interactive web interface
+            - **Pandas**: Data manipulation
+            - **Plotly**: Interactive visualizations
+            - **HuggingFace Datasets**: Dataset metadata
+
+            ---
+
+            **Created by:** AYI-NEDJIMI
+            **Version:** 1.0
+            **Last Updated:** February 2026
+            """)
+
+    # Footer
+    gr.Markdown("""
+    ---
+    💡 **Tip**: Use the search feature to find datasets by specific topics like "NIST", "penetration testing", "threat intelligence", etc.
+    """)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+gradio==4.44.1
+pandas==2.1.4
+plotly==5.18.0
+datasets==2.16.1