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README.md
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---
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license: cc-by-4.0
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-
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configs:
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- config_name: 1024-unrolled
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data_files:
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- split: train
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path:
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- split: valid
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path:
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- split: test
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path:
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-
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- config_name: 2048-unrolled
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data_files:
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- split: train
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path:
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- split: valid
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path:
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- split: test
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path:
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-
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- config_name: 4096-unrolled_n
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data_files:
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- split: train
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path:
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- split: valid
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path:
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- split: test
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path:
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---
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---
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license: cc-by-4.0
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configs:
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- config_name: 1024-unrolled
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data_files:
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- split: train
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path: 1024-unrolled/*train*.tar
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- split: valid
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path: 1024-unrolled/*valid*.tar
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- split: test
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path: 1024-unrolled/*test*.tar
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- config_name: 2048-unrolled
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data_files:
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- split: train
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path: 2048-unrolled/*train*.tar
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- split: valid
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path: 2048-unrolled/*valid*.tar
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- split: test
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path: 2048-unrolled/*test*.tar
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- config_name: 4096-unrolled_n
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data_files:
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- split: train
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path: 4096-unrolled_n/*train*.tar
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- split: valid
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path: 4096-unrolled_n/*valid*.tar
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- split: test
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path: 4096-unrolled_n/*test*.tar
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task_categories:
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- image-classification
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- image-segmentation
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size_categories:
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- 1K<n<10K
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---
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# COSOCO: Compromised Software Containers Image Dataset
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COSOCO (Compromised Software Containers) is a synthetic dataset of 3364 images representing benign
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and malware-compromised software containers. Each image in the dataset represents a dockerized
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software container that has been converted to an image using common byte-to-pixel tools widely used
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in malware analysis. Software container records are labelled (1) **benign** or (2) **compromised**:
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A benign software container will have installed commonly used harmless packages and tools, whereas
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a compromised software container, will have, among harmless benign tools and packages, its underlying
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file system affected by some activated malware instance. Each compromised instance is accompanied by
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a mask, i.e. a black and white image which marks the pixels that correspond to the files of the
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underlying system that have been altered by a malware.
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COSOCO aims to support the identification of compromised software containers via the task of image
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classification task and the identification of compromised files and file system regions inside a
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container via the image segmentation task.
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