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A custom algorithm that takes malware samples as input and converts them into 256x256 bitmap images. Malware of similar families will generate similar looking images. This gives a useful initial visual representation of the malware and can be used to make initial triage easier.

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edwinfredy/Malware-Classification-Using-Image-Processing-Techniques

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Malware Classification Using Image Processing Techniques

This project involves a custom algorithm that takes malware samples as input and converts them into 256x256 bitmap images. Malware of similar families will generate similar looking images. This gives a useful initial visual representation of the malware and can be used to make initial triage easier.
The resulting images can further be used to train ANN models.
To convert a single malware sample into an image, run main.py <filename>.

Sample Results

Image generated from Andromeda trojan, SHA-3529fdeb51006fd75fa7d19d9b686d64de7b2b89f7eb90b5f9e0e83b82693b28.
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Image generated from Guloader malware, SHA-e9aecc07a864c95c949393e7603cfda409a643d86f8cc6da0c1011fdff62f7e0.
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Image generated from Clipbanker trojan, SHA-3241590d83e64c4274595c8d96c9db08df8db169cc54ecde703184ad9da7dc5a.
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A custom algorithm that takes malware samples as input and converts them into 256x256 bitmap images. Malware of similar families will generate similar looking images. This gives a useful initial visual representation of the malware and can be used to make initial triage easier.

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