# CryptoKnight

> CryptoKnight classifies compiled cryptographic primitives. It uses Intel Pin for dynamic instrumentation and PyTorch for the model. The README points to the 2018 paper in Information, DOI 10.3390/info9090231.

- Type: Tool
- Targets: Binary
- Category: Debugging and dynamic analysis
- Relationship: Foundation tool
- Sources reviewed: 2026-11-07
- Added to the catalog: 2026-11-09
- Original resource: <https://github.com/AbertayMachineLearningGroup/CryptoKnight>
- Primary source: <https://github.com/AbertayMachineLearningGroup/CryptoKnight/blob/bdcd10805adacdeee16fb0776103c1f946d5f472/README.md>
- Catalog page: <https://awesomerea.org/resources/cryptoknight/>
- Record JSON: <https://awesomerea.org/resources/cryptoknight/record.json>
- Chinese version: [简体中文](https://awesomerea.org/zh/resources/cryptoknight/index.md)

## Requirements & limits

- Install Python requirements, then run python knight.py --setup. Predicting a binary is python knight.py --predict. OpenSSL headers are needed to compile the sample binaries.
- The README names no license.

## Primary sources

1. <https://github.com/AbertayMachineLearningGroup/CryptoKnight/blob/bdcd10805adacdeee16fb0776103c1f946d5f472/README.md>

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## About this entry

The description was reviewed against primary sources.
Original editorial content: MIT. Linked projects, papers and assets retain their own rights and licenses.