Methodology & Reports
Technical documentation of our intelligence collection, processing, and delivery methodologies
Surikata-X Darknet Intelligence Methodology
DarkScout Intelligence employs automated, scalable collection infrastructure with full audit trails and transparent processing methodologies. Our intelligence gathering is designed for accuracy, coverage, and reliability. Surikata-X represents our flagship darknet intelligence collection system, built for serious analysts and national-level defenders.
Automated Darknet Discovery
AI-trained systems navigate forums, create profiles and personas, complete CAPTCHAs, and automate discovery workflows. Continuous crawling across marketplaces, forums, mixing services, fraud shops, CSAM hubs, narcotics vendors, and closed communities. Full Tor rotation with distributed infrastructure ensures uninterrupted collection.
Multi-Chain Address Extraction
BTC, ETH, Monero, TRON, and Solana addresses captured at scale with strict normalization and entity tagging. Automated extraction from text, images, and structured data sources.
AI-Driven Content Classification
Models trained to detect fraud, narcotics, extremism, CSAM indicators, stolen data, and illicit financial activity. The AI has also been trained to navigate forums, create profiles and personas, complete CAPTCHAs, and automate darknet discovery workflows. GPT-4V fallback handles complex content analysis.
Neural CAPTCHA Solving
AI-trained systems complete CAPTCHAs and navigate protected darknet surfaces. Neural-network CAPTCHA solving with GPT-4V fallback eliminates friction on protected darknet surfaces, maintaining uninterrupted automated collection.
Smart Transaction Learning
Links behavioral patterns, counterparties, and repeated vendor wallet migrations. Identifies wallet reuse patterns and entity relationships across darknet markets.
Post-Processing & Clustering
Every address undergoes behavioral similarity clustering, counterparty graph linkage, cross-market vendor correlation, entity scoring, and feed-ready enrichment.
Intelligence Reports
Public intelligence reports using Surikata-X data and DarkScout Intelligence methodologies.
Funding of CSAM Through Cryptocurrency Exchanges
LatestAnalysis of how major exchanges like Coinbase, Binance, and Revolut inadvertently facilitate funding to CSAM-related addresses, revealing DarkScout's proactive detection capabilities with positive lead times averaging 5.4 days, enabling early intervention.
Read Report →Multi-Chain Attribution Intelligence
Technical deep-dive into cross-chain entity attribution, clustering methodologies, and graph analysis techniques.
Download Report →Behavioral Signal Analysis Framework
Methodology documentation for behavioral pattern detection, anomaly identification, and risk scoring models.
Download Report →Data Schemas & Documentation
Technical specifications, API documentation, and data schema definitions for all intelligence feeds.
Address Attribution Schema
Complete field definitions, data types, and examples for the Address Attribution Dataset.
View Schema →Entity Intelligence Schema
Entity profile structure, relationship mappings, and enrichment field definitions.
View Schema →Surikata-X Feed Schema
Darknet intelligence feed structure, threat category taxonomy, and source metadata fields.
View Schema →API Documentation
REST API reference, authentication, rate limits, and integration examples.
View API Docs →Ready to Access Full Intelligence?
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