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