Fast And Accurate Text Content File Type Identification

2026-09-18

AI Summary

本文提出了一种神经网络模型，用于更准确且快速地识别文本内容文件类型，特别是源代码文件，相比现有工具具有更高的准确性和更快的速度。

Abstract

A common requirement across organizations is to have a tool that can identify file types based on their contents, particularly in the cybersecurity domain where magic numbers and file extensions can not be trusted. While existing tools work well in practice, there is plenty of room for improvement either in terms of computational load and time for detection in the case of model based tools like Magika or in terms of accuracy of detection in the case of file parsing tools that use programming language constructs. In this study, we propose a neural network model for identification of types of text content files, especially source code, that is more accurate and faster than other available tools. Our experiments on open-source files indicate that it is not only more accurate on average for text-content file-type identification, but also approximately four times faster than Magika, while being 28% smaller in size.

Problem

• file type identification
• content-based
• cybersecurity

Innovation

• neural network model
• text content file type identification
• faster and more accurate
• smaller size