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from dagshub.streaming import DagsHubFilesystem
fs = DagsHubFilesystem(".", repo_url="https://dagshub.com/DagsHub-Datasets/sorel-20m-dataset")
fs.listdir("s3://sorel-20m/")
A dataset intended to support research on machine learning techniques for detecting malware. It includes metadata and EMBER-v2 features for approximately 10 million benign and 10 million malicious Portable Executable files, with disarmed but otherwise complete files for all malware samples. All samples are labeled using Sophos in-house labeling methods, have features extracted using the EMBER-v2 feature set, well as metadata obtained via the pefile python library, detection counts obtained via ReversingLabs telemetry, and additional behavioral tags that indicate the rough behavior of the samples.
A dataset intended to support research on machine learning techniques for detecting malware. It includes metadata and EMBER-v2 features for approximately 10 million benign and 10 million malicious Portable Executable files, with disarmed but otherwise complete files for all malware samples. All samples are labeled using Sophos in-house labeling methods, have features extracted using the EMBER-v2 feature set, well as metadata obtained via the pefile python library, detection counts obtained via ReversingLabs telemetry, and additional behavioral tags that indicate the rough behavior of the samples.
At most annually
Sophos AI
aws-pds, cyber security, deep learning, labeled, machine learning
Title: SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection
AuthorName: Richard Harang and Ethan M Rudd
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Are you sure you want to delete this access key?
Are you sure you want to delete this access key?