Our machine learning classification engine processes domain features through multi-layer neural networks, natural language models, and computer vision pipelines to deliver 99.4% accurate categorization across 58 content categories and 180+ languages.
See AI in ActionOur classification engine combines multiple specialized ML models into an ensemble that leverages each model's strengths for different aspects of domain analysis.
Transformer-based NLP models process page text, meta descriptions, titles, and structured data in 180+ languages. The models understand context and semantics, distinguishing between educational health content and pharmaceutical sales, or news reporting versus propaganda.
Convolutional neural networks analyze page screenshots, individual images, and visual layout patterns. The CV pipeline detects explicit imagery, brand logos, product displays, and page design patterns associated with specific content categories.
Graph neural networks analyze domain relationship signals: link graphs, hosting neighborhoods, DNS record patterns, registration data, and traffic correlations. Domains with similar behavioral fingerprints tend to share categories.
Domain name analysis examines character sequences, word boundaries, brand keywords, TLD patterns, and homograph attempts. The lexical engine catches typosquatting, brand impersonation, and DGA-generated domain names at registration time.
Specialized adversarial models trained on millions of confirmed malicious domains detect content signals including obfuscated payloads, fast-flux hosting indicators, bulletproof hosting characteristics, and campaign infrastructure patterns.
A gradient-boosted meta-model combines outputs from all specialized models with confidence weighting. The ensemble approach reduces individual model bias and achieves higher accuracy than any single model, particularly for ambiguous or multi-category domains.
Every domain passes through a multi-stage pipeline that progressively extracts features and applies increasingly sophisticated analysis to produce accurate category assignments.
Crawlers render the domain, extract text in all languages, capture screenshots, and collect DNS records, WHOIS data, and certificate information.
All 12 models in the ensemble process their respective feature sets simultaneously on GPU clusters, producing independent category probability distributions.
The meta-learner weighs each model's output based on its historical accuracy for the predicted category type and produces a final category assignment with accuracy scores.
High-confidence classifications are published immediately. Low-confidence results are queued for human analyst review. Borderline security-sensitive domains are fast-tracked for priority review.
Accuracy measured against human-reviewed validation set of 500,000 domains. Updated quarterly.
Our models improve every day through automated feedback loops, human validation data, and customer recategorization signals that continuously refine classification accuracy.
Models are retrained daily on the latest validated classification data. New domain types, emerging content patterns, and shifting web landscapes are incorporated into the model weights within 24 hours of detection.
Customer recategorization requests and analyst review decisions feed back into the training pipeline. Each human correction improves the model's handling of similar domains, creating a virtuous cycle of increasing accuracy.
Our discovery engine identifies and classifies 250,000+ new domains daily from certificate transparency logs, passive DNS feeds, and web crawling. Each new classification expands the training set for future model iterations.
New model architectures are evaluated through shadow-mode testing where candidate models classify production traffic without affecting results. Only models that demonstrate improved accuracy on holdout sets are promoted to production.
Continuous monitoring tracks classification accuracy, false positive rates, and category drift across all models. Automated alerts trigger review cycles when any metric degrades below established thresholds.
Models are trained against adversarial examples to resist evasion attempts. Malicious actors who try to game classification through cloaking, content manipulation, or hosting tricks find our models resistant to common evasion techniques.
Our machine learning engine classifies domains with industry-leading accuracy. Test it yourself with our free domain lookup tool or integrate directly via our classification API.
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