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Magika: AI powered quick and environment friendly file kind identification

Magika: AI powered quick and environment friendly file kind identification

2024-02-15 19:02:36


At this time we’re open-sourcing Magika, Google’s AI-powered file-type identification system, to assist others precisely detect binary and textual file sorts. Underneath the hood, Magika employs a customized, extremely optimized deep-learning mannequin, enabling exact file identification inside milliseconds, even when working on a CPU.

Magika command line tool used to recognize a identify the type of a diverse set of files
Magika command line instrument used to acknowledge a determine the kind of a various set of information

You’ll be able to try the Magika web demo today, or set up it as a Python library and standalone command line instrument (output is showcased above) by utilizing the usual command line pip set up magika.

Why figuring out file kind is troublesome

For the reason that early days of computing, precisely detecting file sorts has been essential in figuring out methods to course of information. Linux comes outfitted with libmagic and the file utility, which have served because the de facto customary for file kind identification for over 50 years. At this time internet browsers, code editors, and numerous different software program depend on file-type detection to resolve methods to correctly render a file. For instance, trendy code editors use file-type detection to decide on which syntax coloring scheme to make use of because the developer begins typing in a brand new file.

Correct file-type detection is a notoriously troublesome drawback as a result of every file format has a unique construction, or no construction in any respect. That is notably difficult for textual codecs and programming languages as they’ve very related constructs. Thus far, libmagic and most different file-type-identification software program have been counting on a handcrafted assortment of heuristics and customized guidelines to detect every file format.

This handbook method is each time consuming and error inclined as it’s exhausting for people to create generalized guidelines by hand. Particularly for safety functions, creating reliable detection is very difficult as attackers are always trying to confuse detection with adversarially-crafted payloads.

To deal with this subject and supply quick and correct file-type detection we researched and developed Magika, a brand new AI powered file kind detector. Underneath the hood, Magika makes use of a customized, extremely optimized deep-learning mannequin designed and skilled utilizing Keras that solely weighs about 1MB. At inference time Magika makes use of Onnx as an inference engine to make sure information are recognized in a matter of milliseconds, virtually as quick as a non-AI instrument even on CPU.

Magika Efficiency

Magika detection quality compared to other tools on our 1M files benchmark
Magika detection high quality in comparison with different instruments on our 1M information benchmark

Efficiency sensible, Magika, because of its AI mannequin and enormous coaching dataset, is ready to outperform different present instruments by about 20% when evaluated on a 1M information benchmark that encompasses over 100 file sorts. Breaking down by file kind, as reported within the desk under, we see even higher efficiency good points on textual information, together with code information and configuration information that different instruments can battle with.

Table showing various file type identification tools performance for a selection of the file types included in our benchmark
Numerous file kind identification instruments efficiency for a number of the file sorts included in our benchmark – n/a signifies the instrument doesn’t detect the given file kind.

Magika at Google

Internally, Magika is used at scale to assist enhance Google customers’ security by routing Gmail, Drive, and Secure Searching information to the correct safety and content material coverage scanners.
a weekly common of lots of of billions of information reveals that Magika improves file kind identification accuracy by 50% in comparison with our earlier system that relied on handcrafted guidelines. Particularly, this improve in accuracy permits us to scan 11% extra information with our specialized malicious AI document scanners and scale back the variety of unidentified information to three%.

The upcoming integration of Magika with VirusTotal will complement the platform’s present Code Perception performance, which employs Google’s generative AI to research and detect malicious code. Magika will act as a pre-filter earlier than information are analyzed by Code Insight, enhancing the platform’s effectivity and accuracy. This integration, on account of VirusTotal’s collaborative nature, immediately contributes to the worldwide cybersecurity ecosystem, fostering a safer digital surroundings.

Open Sourcing Magika

By open-sourcing Magika, we purpose to assist different software program enhance their file identification accuracy and supply researchers a dependable technique for figuring out file sorts at scale.

See Also

Magika code and model are freely accessible beginning at the moment in Github beneath the Apache2 License. Magika can even shortly be put in as a standalone utility and python library through the pypi package manager by merely typing pip set up magika with no GPU required. We even have an experimental npm package if you need to make use of the TFJS model.

To study extra about methods to use it, please check with Magika documentation site.

Acknowledgements

Magika wouldn’t have been doable with out the assistance of many individuals together with: Ange Albertini, Loua Farah, Francois Galilee, Giancarlo Metitieri, Luca Invernizzi, Younger Maeng, Alex Petit-Bianco, David Tao, Kurt Thomas, Amanda Walker, and Zhixun Tan.

By Elie Bursztein – Cybersecurity AI Technical and Analysis Lead and Yanick Fratantonio – Cybersecurity Analysis Scientist

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