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README.md
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<strong>Real-time AI DNS threat classification by <a href="https://doxx.net">doxx.net</a></strong>
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</p>
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zmsBERT is a fine-tuned BERT model that classifies DNS domain names into 11 threat categories in real time. It catches zero-day phishing, malware, DGA (domain generation algorithm), and other threats that static blocklists miss -
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Validated against **33.5 million real DNS queries** from production servers:
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- **310 zero-day catches** at >=80% confidence
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### The Problem
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Static DNS blocklists are reactive -
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### The Insight
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## Usage
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This model is designed for use with the [ZMS inference engine](https://github.com/doxxcorp/ZMS) -
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```bash
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# Download the model
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---
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<p align="center">
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<a href="https://doxx.net">doxx.net</a> -
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</p>
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<strong>Real-time AI DNS threat classification by <a href="https://doxx.net">doxx.net</a></strong>
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</p>
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zmsBERT is a fine-tuned BERT model that classifies DNS domain names into 11 threat categories in real time. It catches zero-day phishing, malware, DGA (domain generation algorithm), and other threats that static blocklists miss - from the domain name string alone, with no network lookup required.
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Validated against **33.5 million real DNS queries** from production servers:
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- **310 zero-day catches** at >=80% confidence
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### The Problem
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Static DNS blocklists are reactive - a malicious domain must be discovered, reported, analyzed, and added to a list before it's blocked. The window between when an attacker registers a domain and when it appears on blocklists is the **zero-day gap**. zmsBERT closes this gap by classifying domains from their name alone.
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### The Insight
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## Usage
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This model is designed for use with the [ZMS inference engine](https://github.com/doxxcorp/ZMS) - a pure Go BERT implementation with no Python or ONNX dependencies:
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```bash
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# Download the model
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---
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<p align="center">
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<a href="https://doxx.net">doxx.net</a> - Privacy without compromise
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</p>
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