name string | description string | version string | total_entries int64 | modules dict | training dict | technologies list | use_cases list | performance dict | license string | citation string |
|---|---|---|---|---|---|---|---|---|---|---|
Chapel OSINT Ultimate - Intelligence Gathering & Analysis | Comprehensive OSINT dataset with stealth techniques, audio/image detection, and Chapel advanced programming | 1.0 | 30,000 | {
"osint_fundamentals": {
"entries": 10000,
"topics": [
"DNS Reconnaissance (nslookup, dig, PowerShell)",
"WHOIS Intelligence",
"Social Media Intelligence (OSINT framework)",
"Email Pattern Discovery",
"Network Forensics",
"Metadata Extraction (EXIF, documents)",
"Reverse Image Search",
"Geolocation from Images",
"Deepfake Detection"
]
},
"stealth_evasion": {
"entries": 5000,
"topics": [
"Network Stealth (slow scanning, random delays)",
"Memory-only Execution (fileless attacks)",
"Process Hollowing",
"Domain Fronting",
"Anti-forensics Techniques",
"Payload Obfuscation",
"Polymorphic Payloads",
"Encrypted C2 Communication"
]
},
"chapel_advanced": {
"entries": 10000,
"topics": [
"Task Parallelism (cobegin, coforall, begin)",
"Data Parallelism (forall, reduce, scan)",
"Locale Parallelism (distributed computing)",
"Sync Variables (producer-consumer)",
"Atomic Operations",
"GPU Programming (CUDA interop)",
"20+ Chapel Libraries (Socket, JSON, FFT, etc.)",
"Debugging & Profiling (GDB integration)"
]
},
"audio_intelligence": {
"entries": 5000,
"topics": [
"Voice Recognition & Speaker Identification (MFCC)",
"Speech-to-Text (Google API, Whisper)",
"Audio Fingerprinting (Shazam-like algorithm)",
"Sound Classification (CNN, environmental sounds)",
"Audio Forensics (manipulation detection)",
"Voice Activity Detection (VAD)",
"FFT Audio Processing",
"Parallel Audio Analysis with Chapel"
]
},
"image_intelligence": {
"entries": 3000,
"topics": [
"Face Detection & Recognition",
"Object Detection (YOLO, SSD)",
"OCR (Text Extraction)",
"EXIF Metadata Analysis",
"Perceptual Hashing",
"Geolocation from Images",
"Deepfake Detection",
"Landmark Recognition"
]
}
} | {
"model_type": "Chapel OSINT Autoencoder",
"architecture": "384 -> 768 -> 384",
"epochs": 35,
"optimizer": "SGD",
"loss_function": "MSE",
"initial_loss": 0.1973,
"final_loss": 0.1023,
"improvement": "48.1%",
"status": "CONVERGED",
"training_time": "6.7 minutes"
} | [
"Mojo (100% processing)",
"Chapel (Parallel OSINT)",
"PowerShell (Automation & OSINT)",
"Python (Computer Vision, Audio ML)",
"OpenCV, librosa, speech_recognition",
"FFT, CUDA"
] | [
"Open Source Intelligence Gathering",
"Digital Forensics & Incident Response",
"Security Research & Penetration Testing",
"Network Intelligence & Reconnaissance",
"Audio/Visual Intelligence Analysis",
"Distributed Data Collection",
"Parallel Processing for OSINT"
] | {
"generation_speed": "18-20x faster than Python",
"training_time": "6.7 minutes for 10K entries",
"parallel_efficiency": "90%+ with Chapel"
} | MIT | Created with Mojo & Chapel - Ultra-fast OSINT dataset with advanced parallel computing |
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